Hello,
I have just started to learn matlab and tried to solve the following equations:

where,

and the values of alpha(j) will be retrieved from an excel file containing 10 values of it.

Can someone let me know the procedure?

Thank youHello,
I have just started to learn matlab and tried to solve the following equations:

where,

and the values of alpha(j) will be retrieved from an excel file containing 10 values of it.

Can someone let me know the procedure?

Thank you Hello,
I have just started to learn matlab and tried to solve the following equations:

where,

and the values of alpha(j) will be retrieved from an excel file containing 10 values of it.

Can someone let me know the procedure?

Thank you programming, matlab, equation, sum MATLAB Answers — New Questions

​

I am using the Synchronous Machine Model 1.0 in Simulink Simscape.
I want to model a generator and need to change the parameters of the Synchronous Machine Model 1.0 according to the rpm. Currently, I have declared the parameters in the workspace and am importing them into the model.
I have the relationship between rpm and the parameters, I want to change the parameters at every step. How can I do this?I am using the Synchronous Machine Model 1.0 in Simulink Simscape.
I want to model a generator and need to change the parameters of the Synchronous Machine Model 1.0 according to the rpm. Currently, I have declared the parameters in the workspace and am importing them into the model.
I have the relationship between rpm and the parameters, I want to change the parameters at every step. How can I do this? I am using the Synchronous Machine Model 1.0 in Simulink Simscape.
I want to model a generator and need to change the parameters of the Synchronous Machine Model 1.0 according to the rpm. Currently, I have declared the parameters in the workspace and am importing them into the model.
I have the relationship between rpm and the parameters, I want to change the parameters at every step. How can I do this? simulink, simscape, workspace MATLAB Answers — New Questions

​

header line 1 = string
header line 2 = string
line 3:end = numeric data
How do I create a table with two header lines followed by the numeric data?
I have been using table(numeric data, ‘VariableNames’, header line 1) but cannot find a way to have two header lines before the numeric data begins.
Any help would be much appreciated.header line 1 = string
header line 2 = string
line 3:end = numeric data
How do I create a table with two header lines followed by the numeric data?
I have been using table(numeric data, ‘VariableNames’, header line 1) but cannot find a way to have two header lines before the numeric data begins.
Any help would be much appreciated. header line 1 = string
header line 2 = string
line 3:end = numeric data
How do I create a table with two header lines followed by the numeric data?
I have been using table(numeric data, ‘VariableNames’, header line 1) but cannot find a way to have two header lines before the numeric data begins.
Any help would be much appreciated. table MATLAB Answers — New Questions

​

When i try to run the example of "Signal Source Separation Using W-Net Architecture" on MATLAB, it showed "Error: "Error saving to local data stream." when communicating with websave (line 107) 5URL ‘https://ssd.mathworks.com/supportfiles/SPT/data/fetal-ecg-source-separation-testData,zip’.
error matlab,internal.examples.downloadsupportFile(line 48)localfile =websave(localfile,webFilePath);".

The code block :"Download the train and test data sets using the downloadSupportFile function. The data will be unzipped to the tempdir directory. If you want the data at a different location, change trainingDatasetFolder and testDatasetFolder to the desired locations " can’t run, and the detailed code is if trainNetworkFlag
% Download training data set
trainingDatasetZipFile = matlab.internal.examples.downloadSupportFile(‘SPT’,’data/fetal-ecg-source-separation-trainingData.zip’);
trainingDatasetFolder = fullfile(tempdir,’fetal-ecg-source-separation-trainingData’);
if ~exist(trainingDatasetFolder,’dir’)
unzip(trainingDatasetZipFile,trainingDatasetFolder);
end
end

% Download test data set
testDatasetZipFile = matlab.internal.examples.downloadSupportFile(‘SPT’,’data/fetal-ecg-source-separation-testData.zip’);
testDatasetFolder = fullfile(tempdir,’fetal-ecg-source-separation-testData’);
if ~exist(testDatasetFolder,’dir’)
unzip(testDatasetZipFile,testDatasetFolder);
end

Thank you very much for your answers！have a nice day！When i try to run the example of "Signal Source Separation Using W-Net Architecture" on MATLAB, it showed "Error: "Error saving to local data stream." when communicating with websave (line 107) 5URL ‘https://ssd.mathworks.com/supportfiles/SPT/data/fetal-ecg-source-separation-testData,zip’.
error matlab,internal.examples.downloadsupportFile(line 48)localfile =websave(localfile,webFilePath);".

The code block :"Download the train and test data sets using the downloadSupportFile function. The data will be unzipped to the tempdir directory. If you want the data at a different location, change trainingDatasetFolder and testDatasetFolder to the desired locations " can’t run, and the detailed code is if trainNetworkFlag
% Download training data set
trainingDatasetZipFile = matlab.internal.examples.downloadSupportFile(‘SPT’,’data/fetal-ecg-source-separation-trainingData.zip’);
trainingDatasetFolder = fullfile(tempdir,’fetal-ecg-source-separation-trainingData’);
if ~exist(trainingDatasetFolder,’dir’)
unzip(trainingDatasetZipFile,trainingDatasetFolder);
end
end

% Download test data set
testDatasetZipFile = matlab.internal.examples.downloadSupportFile(‘SPT’,’data/fetal-ecg-source-separation-testData.zip’);
testDatasetFolder = fullfile(tempdir,’fetal-ecg-source-separation-testData’);
if ~exist(testDatasetFolder,’dir’)
unzip(testDatasetZipFile,testDatasetFolder);
end

Thank you very much for your answers！have a nice day！ When i try to run the example of "Signal Source Separation Using W-Net Architecture" on MATLAB, it showed "Error: "Error saving to local data stream." when communicating with websave (line 107) 5URL ‘https://ssd.mathworks.com/supportfiles/SPT/data/fetal-ecg-source-separation-testData,zip’.
error matlab,internal.examples.downloadsupportFile(line 48)localfile =websave(localfile,webFilePath);".

The code block :"Download the train and test data sets using the downloadSupportFile function. The data will be unzipped to the tempdir directory. If you want the data at a different location, change trainingDatasetFolder and testDatasetFolder to the desired locations " can’t run, and the detailed code is if trainNetworkFlag
% Download training data set
trainingDatasetZipFile = matlab.internal.examples.downloadSupportFile(‘SPT’,’data/fetal-ecg-source-separation-trainingData.zip’);
trainingDatasetFolder = fullfile(tempdir,’fetal-ecg-source-separation-trainingData’);
if ~exist(trainingDatasetFolder,’dir’)
unzip(trainingDatasetZipFile,trainingDatasetFolder);
end
end

% Download test data set
testDatasetZipFile = matlab.internal.examples.downloadSupportFile(‘SPT’,’data/fetal-ecg-source-separation-testData.zip’);
testDatasetFolder = fullfile(tempdir,’fetal-ecg-source-separation-testData’);
if ~exist(testDatasetFolder,’dir’)
unzip(testDatasetZipFile,testDatasetFolder);
end

Thank you very much for your answers！have a nice day！ wnet, ecg signal, neural network, signal separate, matlab code MATLAB Answers — New Questions

​

i’m working on optimising a design of a hybrid PV/Wind energy system (with battery) using Genetic Algorithms ,and based on a research paper i have been able to code the following :
% Solar PV Generator script
function solargen = solar(Areapv)
% Input:
% Areapv = total area of all PV modules, one of the variables to be optimized
% x = solar irradiance (data read from an Excel file)
x = xlsread(‘éclairement_tlemcen.xlsx’,’A1:A8784′);
effpv = 0.227; % efficiency of the PV module specified by manufacturer
% Output:
% solargen = power produced by the PV generator (kW)
% solargen = Areapv * effpv * Irrad * 0.001;
w = effpv * Areapv * 0.001;
solargen = w .* x;
end
wind turbine script
% Wind Generator script
function windgen = windpower(Areawt)
% Inputs:
% Areawt = total rotor swept area of all wind turbines used (m2)
% Areawt is one of the variables to be optimized
coeff_wt = 45/100; % coefficient of power of wind turbine
rho = 1.1839; % density of air (kg/m3) at 1 atm pres and 25 degC temp
vci = 2.01168; % wind turbine cut-in/min wind speed (m/s)
vrat = 13.8582; % wind turbine rated wind speed (m/s)
vco = 17.8816; % wind turbine cut-out/max wind speed (m/s)
% vact = actual wind speed (data read from an Excel file)
vact = xlsread(‘wind_speed_tlemcen.xlsx’,’A1:A8784′);

y = length(vact); % number of wind speed data points
% windgen = power produced by the wind generator (kW)
b = zeros(1, y); % preallocation of memory outside loop
windgen = b.’;
for a = 1:y
if ((vact(a) < vci)||(vact(a) > vco)) % wind speed less than cut-in or greater than cut-out
z = 0;
elseif ((vact(a) >= vci)&&(vact(a) < vrat)) % wind speed between cut-in and rated
z = 0.5 * coeff_wt * rho * Areawt * (vrat.^3) * ((vact(a).^3 – vci.^3)/(vrat.^3 – vci.^3)) * 0.001;

else % wind speed between rated and cut-out
z = 0.5 * coeff_wt * rho * Areawt * (vrat.^3) * 0.001;
end
windgen(a) = z;
end
end
battery script
% Battery System script

function [battpower,Pload] = battery(Areapv,Areawt,Capbatt)
% Inputs:
% Areapv = total area of all PV modules used (m2)
% Areawt = total rotor swept area of all wind turbines used (m2)
% Capbatt = total energy capacity of all batteries used (kWh)
% The three inputs are the variables to be optimized
% Pload = load demand (data read from an Excel file)
Pl = xlsread(‘charge_tlemcen.xlsx’,’A1:A8784′);

Pload = 0.001 * Pl; % load power in kW
eff_inv = 88/100; % efficiency of inverter
eff_batt_cha = 92/100; % efficiency of battery charging
eff_batt_disch = 100/100; % efficiency of battery discharging
Ppv = solar(Areapv); % generation from solar
Pwind = windpower(Areawt); % generation from wind
soc_max = 98/100; % maximum state of charge of battery
dod_max = 80/100; % maximum depth of discharge
y = length(Pload);
% preallocation of memory outside loop for some variables
c = zeros(1, y);
Pgen = c.’;
d = zeros(1, y);
soc = d.’;
e = zeros(1, y);
dod = e.’;
f = zeros(1, y);
Pdump = f.’;
g = zeros(1, y);
battpower = g.’;
h = zeros(1, y);
Pdef = h.’;
soc(1) = soc_max; % initial state of charge of battery
dod(1) = 1 – soc(1); % initial state of discharge of battery
for b = 1:y
Pgen(b) = Ppv(b) + Pwind(b);
soc(b) = soc(b) + (battpower(b)/(1000*Capbatt));
if Pgen(b) > (Pload(b)/eff_inv) % generation > load
if soc(b) < soc_max % battery not charged fully
battpower(b) = (Pgen(b) – (Pload(b)/eff_inv)) *eff_batt_cha; % battery charges, battpower is +ve
else
soc(b) >= soc_max; % battery charged to maximum
battpower(b) = 0; % no more charging
Pdump(b) = Pgen(b) – (Pload(b)/eff_inv);
% surplus power is dumped after battery charges to maximum
end
elseif Pgen(b) < (Pload(b)/eff_inv) % generation < load
if (dod(b)) < dod_max % battery not dicharged to maximum
battpower(b) = -((Pload(b)/eff_inv) – Pgen(b)) * eff_batt_disch; % battery discharges, battpower is -ve
else
dod(b) >= dod_max; % battery discharged to maximum
battpower(b) = 0; % no more discharging
Pdef(b) = Pload(b) – (Pgen(b) + ((1000 * Capbatt)* ((soc(b)-(1-dod_max))))* eff_inv);
% deficit power persists after battery discharged fully
end
else % Pgen(b) = (Pload(b)/eff_inv) i.e. generation = load
battpower(b) = 0; % No charging or discharging
end
end
end
LPSP script which is the non linear constraint script
function [LPSP_value,LPSP_eq] = LPSP(Areapv,Areawt,Capbatt)
eff_inv = 88/100; % efficiency of inverter
s = solar(Areapv); % call solar function
w = windpower(Areawt); % call wind function
Pg = s + w; % generation = solar + wind
[b,Pl] = battery(Areapv,Areawt,Capbatt); % call battery function
Pgen = sum(Pg); % total generation
Pload = sum(Pl); % total load
battp = sum(b); % total battery power

LPS = sum(Pdef);
LPSP_value = (LPS / Pload) – 0.05; % LPSP <= 0.05
LPSP_eq = [];
end
the cost script which is the objective function
% Total System Cost script

% The total system cost is the sum of the following costs:
% Capital/investment costs
% Operation and maintenance costs
% Replacement costs
% Salvage revenue (negative cost)

% The present value of the above cost components are found for each of the
% three main system components: solar pv generator, wind generator and
% battery system; all costs are then added to get the total system cost

% The total system cost is the objective function to be optimized by a
% genetic algorithm. The constraints of this function are the loss of power
% supply probability (defined in a separate function) and the input
% variables.

function system_cost = cost(Areapv,Areawt,Capbatt)
%————————————————————————-%
% Constraints
Areapv_max = 20 * 1.63; % 20 PV modules maximum (32.6 m2 max area)
Areapv(Areapv>Areapv_max) = Areapv_max;
Areapv_min = 10 * 1.63; % 10 PV modules minimum (16.3 m2 min area)
Areapv(Areapv<Areapv_min) = Areapv_min;
Areawt_max = 10 * pi * (0.85344.^2); % 10 wind turbines maximum
% (22.8821 m2 max area)
Areawt(Areawt>Areawt_max) = Areawt_max;
Areawt_min = 3 * pi * (0.85344.^2); % 3 wind turbines minimum
% (6.8646 m2 min area)
Areawt(Areawt<Areawt_min) = Areawt_min;
Capbatt_max = 20 * 1.68; % 20 battery units maximum (33.6 kWh max)
Capbatt(Capbatt>Capbatt_max) = Capbatt_max;
Capbatt_min = 10 * 1.68; % 10 battery units minimum (16.8 kWh min)
Capbatt(Capbatt<Capbatt_min) = Capbatt_min;
%————————————————————————-%
% Project lifetime
%proj_life = 25; % years
%————————————————————————-%
% Rates applicable
%int = 5/100; % interest rate that affects all costs
%infl = 3/100; % inflation rate that affects salvage costs
%inc = 4/100; % non-inflation rate at which non-salvage costs increase
%————————————————————————-%
% Solar specifications
cap_pv = 300/1.63; % capital cost of PV module (184.0491 UK pounds/m2)
oandm_pv = 7.5/1.63; % o & m cost of PV module (4.6012 UK pounds/m2/yr)
sal_pv = 60/1.63; % salvage revenue of PV module (36.8098 UK pounds/m2)
%life_pv = 25; % lifetime of PV module (years)
%rep_pv = (proj_life / life_pv) – 1; % number of replacements in project (0)
%————————————————————————-%
% Wind specifications
cap_wt = 1125/(pi*(0.85344.^2)); % capital cost of turbine
% (491.6507 UK pounds/m2)
oandm_wt = 168.75/(pi*(0.85344.^2)); % o & m cost of turbine
% (73.7476 UK pounds/m2/yr)
sal_wt = 225/(pi*(0.85344.^2)); % salvage revenue of turbine
% (98.3301 UK pounds/m2)
%life_wt = 12.5; % lifetime of turbine (years)
%rep_wt = (proj_life / life_wt) – 1; % number of replacements in project (1)
%————————————————————————-%
% Battery specifications
cap_batt = 364/1.68; % capital cost of battery (216.6667 UK pounds/kWh)
oandm_batt = 3.64/1.68; % o & m cost of battery (2.1667 UK pounds/kWh/yr)
sal_batt = 36.4/1.68; % salvage revenue of battery (21.6667 UK pounds/kWh)
%life_batt = 2.5; % lifetime of battery (years)
%rep_batt = (proj_life / life_batt) – 1; % number of replacements in project;
% (9)
%————————————————————————-%
% Useful factors for net present value
%fac1 = (1+inc)/(1+int); % 0.9905
%fac2 = (1+infl)/(1+int); % 0.9810
%factor1a = symsum(fac1.^((k-1)*life_wt),k,1,rep_wt);
factor1a = 1; % summation of fac1^(k-1)*life_wt) for turbine replacements
%factor1b = symsum(fac1.^((k-1)*life_batt),k,1,rep_batt);
factor1b = 8.1943; % summation of fac1.^((k-1)*life_batt) for battery
% replacements
%factor2 = symsum(fac1.^k,k,1,proj_life);
factor2 = 22.1282; % summation of fac1^k for project life
% factor2 = fac1 + (fac1.^2) + (fac1.^3) + … + (fac1.^25)
%factor3a = ((1+infl).^proj_life)/((1+int).^proj_life);
factor3a = 0.6183;
%factor3b = symsum(fac2.^(x*life_wt),x,1,rep_wt);
factor3b = 0.7863; % summation of fac2^(x*life_wt) for turbine life
%factor3c = symsum(fac2.^(x*life_batt),x,1,rep_batt);
factor3c = 7.1315; % summation of fac2^(x*life_batt) for battery life
%————————————————————————-%
% Capital costs and replacement costs
% Solar
pv_caprep = cap_pv * Areapv;
pv_caprep_npv = pv_caprep;
const_pv1 = pv_caprep_npv / Areapv;
% Wind
windg_caprep = cap_wt * Areawt;
windg_caprep_npv = windg_caprep * factor1a;
const_wt1 = windg_caprep_npv / Areawt;
% Battery
batt_caprep = cap_batt * Capbatt;
batt_caprep_npv = batt_caprep * factor1b;
const_batt1 = batt_caprep_npv / Capbatt;
%————————————————————————-%
% Operation and maintenance costs
% Solar
pv_oandm = oandm_pv * Areapv;
pv_oandm_npv = pv_oandm * factor2;
const_pv2 = pv_oandm_npv / Areapv;
% Wind
windg_oandm = oandm_wt * Areawt;
windg_oandm_npv = windg_oandm * factor2;
const_wt2 = windg_oandm_npv / Areawt;
% Battery
batt_oandm = oandm_batt * Capbatt;
batt_oandm_npv = batt_oandm * factor2;
const_batt2 = batt_oandm_npv / Capbatt;
%————————————————————————-%
% Salvage revenues
% Solar
pv_sal = sal_pv * Areapv;
pv_sal_npv = pv_sal * factor3a;
const_pv3 = pv_sal_npv / Areapv;
% Wind
windg_sal = sal_wt * Areawt;
windg_sal_npv = windg_sal * factor3b;
const_wt3 = windg_sal_npv / Areawt;
% Battery
batt_sal = sal_batt * Capbatt;
batt_sal_npv = batt_sal * factor3c;
const_batt3 = batt_sal_npv / Capbatt;
%————————————————————————-%
% Total system cost
% In general: system cost = capital cost + o&m cost – salvage revenue
system_cost = ((const_pv1 + const_pv2 – const_pv3) * Areapv) + …
((const_wt1 + const_wt2 – const_wt3) * Areawt) +((const_batt1 + const_batt2 – const_batt3) * Capbatt);
end
the ga script
nvars = 3; % number of input variables
fun = @cost; % objective function to be optimized (system cost)
lb = [16.3 6.865 16.8]; % lower bounds of input variables
ub = [32.6 22.882 33.6]; % lower bounds of input variables
nonlcon = @LPSP; % nonlinear constraint function (loss of power supply
% probability
% Optimization command
[X,fval] = ga(fun,nvars,[],[],[],[],lb,ub,nonlcon);
i can provide the exel data files you need to run the function

these are the errors i could not fix :
Error using .*
Matrix dimensions must agree.

Error in solar (line 12)
solargen = w .* x;

Error in LPSP (line 9)
s = solar(Areapv); % call solar function

Error in createAnonymousFcn>@(x)fcn(x,FcnArgs{:}) (line 11)
fcn_handle = @(x) fcn(x,FcnArgs{:});

Error in constrValidate (line 23)
[cineq,ceq] = nonlcon(Iterate.x’);

Error in gacommon (line 132)
[LinearConstr, Iterate,nineqcstr,neqcstr,ncstr] = constrValidate(NonconFcn, …

Error in ga (line 336)
NonconFcn,options,Iterate,type] = gacommon(nvars,fun,Aineq,bineq,Aeq,beq,lb,ub, …

Error in gen (line 28)
[X,fval] = ga(fun,nvars,[],[],[],[],lb,ub,nonlcon);

Caused by:
Failure in initial user-supplied nonlinear constraint function evaluation.i’m working on optimising a design of a hybrid PV/Wind energy system (with battery) using Genetic Algorithms ,and based on a research paper i have been able to code the following :
% Solar PV Generator script
function solargen = solar(Areapv)
% Input:
% Areapv = total area of all PV modules, one of the variables to be optimized
% x = solar irradiance (data read from an Excel file)
x = xlsread(‘éclairement_tlemcen.xlsx’,’A1:A8784′);
effpv = 0.227; % efficiency of the PV module specified by manufacturer
% Output:
% solargen = power produced by the PV generator (kW)
% solargen = Areapv * effpv * Irrad * 0.001;
w = effpv * Areapv * 0.001;
solargen = w .* x;
end
wind turbine script
% Wind Generator script
function windgen = windpower(Areawt)
% Inputs:
% Areawt = total rotor swept area of all wind turbines used (m2)
% Areawt is one of the variables to be optimized
coeff_wt = 45/100; % coefficient of power of wind turbine
rho = 1.1839; % density of air (kg/m3) at 1 atm pres and 25 degC temp
vci = 2.01168; % wind turbine cut-in/min wind speed (m/s)
vrat = 13.8582; % wind turbine rated wind speed (m/s)
vco = 17.8816; % wind turbine cut-out/max wind speed (m/s)
% vact = actual wind speed (data read from an Excel file)
vact = xlsread(‘wind_speed_tlemcen.xlsx’,’A1:A8784′);

y = length(vact); % number of wind speed data points
% windgen = power produced by the wind generator (kW)
b = zeros(1, y); % preallocation of memory outside loop
windgen = b.’;
for a = 1:y
if ((vact(a) < vci)||(vact(a) > vco)) % wind speed less than cut-in or greater than cut-out
z = 0;
elseif ((vact(a) >= vci)&&(vact(a) < vrat)) % wind speed between cut-in and rated
z = 0.5 * coeff_wt * rho * Areawt * (vrat.^3) * ((vact(a).^3 – vci.^3)/(vrat.^3 – vci.^3)) * 0.001;

else % wind speed between rated and cut-out
z = 0.5 * coeff_wt * rho * Areawt * (vrat.^3) * 0.001;
end
windgen(a) = z;
end
end
battery script
% Battery System script

function [battpower,Pload] = battery(Areapv,Areawt,Capbatt)
% Inputs:
% Areapv = total area of all PV modules used (m2)
% Areawt = total rotor swept area of all wind turbines used (m2)
% Capbatt = total energy capacity of all batteries used (kWh)
% The three inputs are the variables to be optimized
% Pload = load demand (data read from an Excel file)
Pl = xlsread(‘charge_tlemcen.xlsx’,’A1:A8784′);

Pload = 0.001 * Pl; % load power in kW
eff_inv = 88/100; % efficiency of inverter
eff_batt_cha = 92/100; % efficiency of battery charging
eff_batt_disch = 100/100; % efficiency of battery discharging
Ppv = solar(Areapv); % generation from solar
Pwind = windpower(Areawt); % generation from wind
soc_max = 98/100; % maximum state of charge of battery
dod_max = 80/100; % maximum depth of discharge
y = length(Pload);
% preallocation of memory outside loop for some variables
c = zeros(1, y);
Pgen = c.’;
d = zeros(1, y);
soc = d.’;
e = zeros(1, y);
dod = e.’;
f = zeros(1, y);
Pdump = f.’;
g = zeros(1, y);
battpower = g.’;
h = zeros(1, y);
Pdef = h.’;
soc(1) = soc_max; % initial state of charge of battery
dod(1) = 1 – soc(1); % initial state of discharge of battery
for b = 1:y
Pgen(b) = Ppv(b) + Pwind(b);
soc(b) = soc(b) + (battpower(b)/(1000*Capbatt));
if Pgen(b) > (Pload(b)/eff_inv) % generation > load
if soc(b) < soc_max % battery not charged fully
battpower(b) = (Pgen(b) – (Pload(b)/eff_inv)) *eff_batt_cha; % battery charges, battpower is +ve
else
soc(b) >= soc_max; % battery charged to maximum
battpower(b) = 0; % no more charging
Pdump(b) = Pgen(b) – (Pload(b)/eff_inv);
% surplus power is dumped after battery charges to maximum
end
elseif Pgen(b) < (Pload(b)/eff_inv) % generation < load
if (dod(b)) < dod_max % battery not dicharged to maximum
battpower(b) = -((Pload(b)/eff_inv) – Pgen(b)) * eff_batt_disch; % battery discharges, battpower is -ve
else
dod(b) >= dod_max; % battery discharged to maximum
battpower(b) = 0; % no more discharging
Pdef(b) = Pload(b) – (Pgen(b) + ((1000 * Capbatt)* ((soc(b)-(1-dod_max))))* eff_inv);
% deficit power persists after battery discharged fully
end
else % Pgen(b) = (Pload(b)/eff_inv) i.e. generation = load
battpower(b) = 0; % No charging or discharging
end
end
end
LPSP script which is the non linear constraint script
function [LPSP_value,LPSP_eq] = LPSP(Areapv,Areawt,Capbatt)
eff_inv = 88/100; % efficiency of inverter
s = solar(Areapv); % call solar function
w = windpower(Areawt); % call wind function
Pg = s + w; % generation = solar + wind
[b,Pl] = battery(Areapv,Areawt,Capbatt); % call battery function
Pgen = sum(Pg); % total generation
Pload = sum(Pl); % total load
battp = sum(b); % total battery power

LPS = sum(Pdef);
LPSP_value = (LPS / Pload) – 0.05; % LPSP <= 0.05
LPSP_eq = [];
end
the cost script which is the objective function
% Total System Cost script

% The total system cost is the sum of the following costs:
% Capital/investment costs
% Operation and maintenance costs
% Replacement costs
% Salvage revenue (negative cost)

% The present value of the above cost components are found for each of the
% three main system components: solar pv generator, wind generator and
% battery system; all costs are then added to get the total system cost

% The total system cost is the objective function to be optimized by a
% genetic algorithm. The constraints of this function are the loss of power
% supply probability (defined in a separate function) and the input
% variables.

function system_cost = cost(Areapv,Areawt,Capbatt)
%————————————————————————-%
% Constraints
Areapv_max = 20 * 1.63; % 20 PV modules maximum (32.6 m2 max area)
Areapv(Areapv>Areapv_max) = Areapv_max;
Areapv_min = 10 * 1.63; % 10 PV modules minimum (16.3 m2 min area)
Areapv(Areapv<Areapv_min) = Areapv_min;
Areawt_max = 10 * pi * (0.85344.^2); % 10 wind turbines maximum
% (22.8821 m2 max area)
Areawt(Areawt>Areawt_max) = Areawt_max;
Areawt_min = 3 * pi * (0.85344.^2); % 3 wind turbines minimum
% (6.8646 m2 min area)
Areawt(Areawt<Areawt_min) = Areawt_min;
Capbatt_max = 20 * 1.68; % 20 battery units maximum (33.6 kWh max)
Capbatt(Capbatt>Capbatt_max) = Capbatt_max;
Capbatt_min = 10 * 1.68; % 10 battery units minimum (16.8 kWh min)
Capbatt(Capbatt<Capbatt_min) = Capbatt_min;
%————————————————————————-%
% Project lifetime
%proj_life = 25; % years
%————————————————————————-%
% Rates applicable
%int = 5/100; % interest rate that affects all costs
%infl = 3/100; % inflation rate that affects salvage costs
%inc = 4/100; % non-inflation rate at which non-salvage costs increase
%————————————————————————-%
% Solar specifications
cap_pv = 300/1.63; % capital cost of PV module (184.0491 UK pounds/m2)
oandm_pv = 7.5/1.63; % o & m cost of PV module (4.6012 UK pounds/m2/yr)
sal_pv = 60/1.63; % salvage revenue of PV module (36.8098 UK pounds/m2)
%life_pv = 25; % lifetime of PV module (years)
%rep_pv = (proj_life / life_pv) – 1; % number of replacements in project (0)
%————————————————————————-%
% Wind specifications
cap_wt = 1125/(pi*(0.85344.^2)); % capital cost of turbine
% (491.6507 UK pounds/m2)
oandm_wt = 168.75/(pi*(0.85344.^2)); % o & m cost of turbine
% (73.7476 UK pounds/m2/yr)
sal_wt = 225/(pi*(0.85344.^2)); % salvage revenue of turbine
% (98.3301 UK pounds/m2)
%life_wt = 12.5; % lifetime of turbine (years)
%rep_wt = (proj_life / life_wt) – 1; % number of replacements in project (1)
%————————————————————————-%
% Battery specifications
cap_batt = 364/1.68; % capital cost of battery (216.6667 UK pounds/kWh)
oandm_batt = 3.64/1.68; % o & m cost of battery (2.1667 UK pounds/kWh/yr)
sal_batt = 36.4/1.68; % salvage revenue of battery (21.6667 UK pounds/kWh)
%life_batt = 2.5; % lifetime of battery (years)
%rep_batt = (proj_life / life_batt) – 1; % number of replacements in project;
% (9)
%————————————————————————-%
% Useful factors for net present value
%fac1 = (1+inc)/(1+int); % 0.9905
%fac2 = (1+infl)/(1+int); % 0.9810
%factor1a = symsum(fac1.^((k-1)*life_wt),k,1,rep_wt);
factor1a = 1; % summation of fac1^(k-1)*life_wt) for turbine replacements
%factor1b = symsum(fac1.^((k-1)*life_batt),k,1,rep_batt);
factor1b = 8.1943; % summation of fac1.^((k-1)*life_batt) for battery
% replacements
%factor2 = symsum(fac1.^k,k,1,proj_life);
factor2 = 22.1282; % summation of fac1^k for project life
% factor2 = fac1 + (fac1.^2) + (fac1.^3) + … + (fac1.^25)
%factor3a = ((1+infl).^proj_life)/((1+int).^proj_life);
factor3a = 0.6183;
%factor3b = symsum(fac2.^(x*life_wt),x,1,rep_wt);
factor3b = 0.7863; % summation of fac2^(x*life_wt) for turbine life
%factor3c = symsum(fac2.^(x*life_batt),x,1,rep_batt);
factor3c = 7.1315; % summation of fac2^(x*life_batt) for battery life
%————————————————————————-%
% Capital costs and replacement costs
% Solar
pv_caprep = cap_pv * Areapv;
pv_caprep_npv = pv_caprep;
const_pv1 = pv_caprep_npv / Areapv;
% Wind
windg_caprep = cap_wt * Areawt;
windg_caprep_npv = windg_caprep * factor1a;
const_wt1 = windg_caprep_npv / Areawt;
% Battery
batt_caprep = cap_batt * Capbatt;
batt_caprep_npv = batt_caprep * factor1b;
const_batt1 = batt_caprep_npv / Capbatt;
%————————————————————————-%
% Operation and maintenance costs
% Solar
pv_oandm = oandm_pv * Areapv;
pv_oandm_npv = pv_oandm * factor2;
const_pv2 = pv_oandm_npv / Areapv;
% Wind
windg_oandm = oandm_wt * Areawt;
windg_oandm_npv = windg_oandm * factor2;
const_wt2 = windg_oandm_npv / Areawt;
% Battery
batt_oandm = oandm_batt * Capbatt;
batt_oandm_npv = batt_oandm * factor2;
const_batt2 = batt_oandm_npv / Capbatt;
%————————————————————————-%
% Salvage revenues
% Solar
pv_sal = sal_pv * Areapv;
pv_sal_npv = pv_sal * factor3a;
const_pv3 = pv_sal_npv / Areapv;
% Wind
windg_sal = sal_wt * Areawt;
windg_sal_npv = windg_sal * factor3b;
const_wt3 = windg_sal_npv / Areawt;
% Battery
batt_sal = sal_batt * Capbatt;
batt_sal_npv = batt_sal * factor3c;
const_batt3 = batt_sal_npv / Capbatt;
%————————————————————————-%
% Total system cost
% In general: system cost = capital cost + o&m cost – salvage revenue
system_cost = ((const_pv1 + const_pv2 – const_pv3) * Areapv) + …
((const_wt1 + const_wt2 – const_wt3) * Areawt) +((const_batt1 + const_batt2 – const_batt3) * Capbatt);
end
the ga script
nvars = 3; % number of input variables
fun = @cost; % objective function to be optimized (system cost)
lb = [16.3 6.865 16.8]; % lower bounds of input variables
ub = [32.6 22.882 33.6]; % lower bounds of input variables
nonlcon = @LPSP; % nonlinear constraint function (loss of power supply
% probability
% Optimization command
[X,fval] = ga(fun,nvars,[],[],[],[],lb,ub,nonlcon);
i can provide the exel data files you need to run the function

these are the errors i could not fix :
Error using .*
Matrix dimensions must agree.

Error in solar (line 12)
solargen = w .* x;

Error in LPSP (line 9)
s = solar(Areapv); % call solar function

Error in createAnonymousFcn>@(x)fcn(x,FcnArgs{:}) (line 11)
fcn_handle = @(x) fcn(x,FcnArgs{:});

Error in constrValidate (line 23)
[cineq,ceq] = nonlcon(Iterate.x’);

Error in gacommon (line 132)
[LinearConstr, Iterate,nineqcstr,neqcstr,ncstr] = constrValidate(NonconFcn, …

Error in ga (line 336)
NonconFcn,options,Iterate,type] = gacommon(nvars,fun,Aineq,bineq,Aeq,beq,lb,ub, …

Error in gen (line 28)
[X,fval] = ga(fun,nvars,[],[],[],[],lb,ub,nonlcon);

Caused by:
Failure in initial user-supplied nonlinear constraint function evaluation. i’m working on optimising a design of a hybrid PV/Wind energy system (with battery) using Genetic Algorithms ,and based on a research paper i have been able to code the following :
% Solar PV Generator script
function solargen = solar(Areapv)
% Input:
% Areapv = total area of all PV modules, one of the variables to be optimized
% x = solar irradiance (data read from an Excel file)
x = xlsread(‘éclairement_tlemcen.xlsx’,’A1:A8784′);
effpv = 0.227; % efficiency of the PV module specified by manufacturer
% Output:
% solargen = power produced by the PV generator (kW)
% solargen = Areapv * effpv * Irrad * 0.001;
w = effpv * Areapv * 0.001;
solargen = w .* x;
end
wind turbine script
% Wind Generator script
function windgen = windpower(Areawt)
% Inputs:
% Areawt = total rotor swept area of all wind turbines used (m2)
% Areawt is one of the variables to be optimized
coeff_wt = 45/100; % coefficient of power of wind turbine
rho = 1.1839; % density of air (kg/m3) at 1 atm pres and 25 degC temp
vci = 2.01168; % wind turbine cut-in/min wind speed (m/s)
vrat = 13.8582; % wind turbine rated wind speed (m/s)
vco = 17.8816; % wind turbine cut-out/max wind speed (m/s)
% vact = actual wind speed (data read from an Excel file)
vact = xlsread(‘wind_speed_tlemcen.xlsx’,’A1:A8784′);

y = length(vact); % number of wind speed data points
% windgen = power produced by the wind generator (kW)
b = zeros(1, y); % preallocation of memory outside loop
windgen = b.’;
for a = 1:y
if ((vact(a) < vci)||(vact(a) > vco)) % wind speed less than cut-in or greater than cut-out
z = 0;
elseif ((vact(a) >= vci)&&(vact(a) < vrat)) % wind speed between cut-in and rated
z = 0.5 * coeff_wt * rho * Areawt * (vrat.^3) * ((vact(a).^3 – vci.^3)/(vrat.^3 – vci.^3)) * 0.001;

else % wind speed between rated and cut-out
z = 0.5 * coeff_wt * rho * Areawt * (vrat.^3) * 0.001;
end
windgen(a) = z;
end
end
battery script
% Battery System script

function [battpower,Pload] = battery(Areapv,Areawt,Capbatt)
% Inputs:
% Areapv = total area of all PV modules used (m2)
% Areawt = total rotor swept area of all wind turbines used (m2)
% Capbatt = total energy capacity of all batteries used (kWh)
% The three inputs are the variables to be optimized
% Pload = load demand (data read from an Excel file)
Pl = xlsread(‘charge_tlemcen.xlsx’,’A1:A8784′);

Pload = 0.001 * Pl; % load power in kW
eff_inv = 88/100; % efficiency of inverter
eff_batt_cha = 92/100; % efficiency of battery charging
eff_batt_disch = 100/100; % efficiency of battery discharging
Ppv = solar(Areapv); % generation from solar
Pwind = windpower(Areawt); % generation from wind
soc_max = 98/100; % maximum state of charge of battery
dod_max = 80/100; % maximum depth of discharge
y = length(Pload);
% preallocation of memory outside loop for some variables
c = zeros(1, y);
Pgen = c.’;
d = zeros(1, y);
soc = d.’;
e = zeros(1, y);
dod = e.’;
f = zeros(1, y);
Pdump = f.’;
g = zeros(1, y);
battpower = g.’;
h = zeros(1, y);
Pdef = h.’;
soc(1) = soc_max; % initial state of charge of battery
dod(1) = 1 – soc(1); % initial state of discharge of battery
for b = 1:y
Pgen(b) = Ppv(b) + Pwind(b);
soc(b) = soc(b) + (battpower(b)/(1000*Capbatt));
if Pgen(b) > (Pload(b)/eff_inv) % generation > load
if soc(b) < soc_max % battery not charged fully
battpower(b) = (Pgen(b) – (Pload(b)/eff_inv)) *eff_batt_cha; % battery charges, battpower is +ve
else
soc(b) >= soc_max; % battery charged to maximum
battpower(b) = 0; % no more charging
Pdump(b) = Pgen(b) – (Pload(b)/eff_inv);
% surplus power is dumped after battery charges to maximum
end
elseif Pgen(b) < (Pload(b)/eff_inv) % generation < load
if (dod(b)) < dod_max % battery not dicharged to maximum
battpower(b) = -((Pload(b)/eff_inv) – Pgen(b)) * eff_batt_disch; % battery discharges, battpower is -ve
else
dod(b) >= dod_max; % battery discharged to maximum
battpower(b) = 0; % no more discharging
Pdef(b) = Pload(b) – (Pgen(b) + ((1000 * Capbatt)* ((soc(b)-(1-dod_max))))* eff_inv);
% deficit power persists after battery discharged fully
end
else % Pgen(b) = (Pload(b)/eff_inv) i.e. generation = load
battpower(b) = 0; % No charging or discharging
end
end
end
LPSP script which is the non linear constraint script
function [LPSP_value,LPSP_eq] = LPSP(Areapv,Areawt,Capbatt)
eff_inv = 88/100; % efficiency of inverter
s = solar(Areapv); % call solar function
w = windpower(Areawt); % call wind function
Pg = s + w; % generation = solar + wind
[b,Pl] = battery(Areapv,Areawt,Capbatt); % call battery function
Pgen = sum(Pg); % total generation
Pload = sum(Pl); % total load
battp = sum(b); % total battery power

LPS = sum(Pdef);
LPSP_value = (LPS / Pload) – 0.05; % LPSP <= 0.05
LPSP_eq = [];
end
the cost script which is the objective function
% Total System Cost script

% The total system cost is the sum of the following costs:
% Capital/investment costs
% Operation and maintenance costs
% Replacement costs
% Salvage revenue (negative cost)

% The present value of the above cost components are found for each of the
% three main system components: solar pv generator, wind generator and
% battery system; all costs are then added to get the total system cost

% The total system cost is the objective function to be optimized by a
% genetic algorithm. The constraints of this function are the loss of power
% supply probability (defined in a separate function) and the input
% variables.

function system_cost = cost(Areapv,Areawt,Capbatt)
%————————————————————————-%
% Constraints
Areapv_max = 20 * 1.63; % 20 PV modules maximum (32.6 m2 max area)
Areapv(Areapv>Areapv_max) = Areapv_max;
Areapv_min = 10 * 1.63; % 10 PV modules minimum (16.3 m2 min area)
Areapv(Areapv<Areapv_min) = Areapv_min;
Areawt_max = 10 * pi * (0.85344.^2); % 10 wind turbines maximum
% (22.8821 m2 max area)
Areawt(Areawt>Areawt_max) = Areawt_max;
Areawt_min = 3 * pi * (0.85344.^2); % 3 wind turbines minimum
% (6.8646 m2 min area)
Areawt(Areawt<Areawt_min) = Areawt_min;
Capbatt_max = 20 * 1.68; % 20 battery units maximum (33.6 kWh max)
Capbatt(Capbatt>Capbatt_max) = Capbatt_max;
Capbatt_min = 10 * 1.68; % 10 battery units minimum (16.8 kWh min)
Capbatt(Capbatt<Capbatt_min) = Capbatt_min;
%————————————————————————-%
% Project lifetime
%proj_life = 25; % years
%————————————————————————-%
% Rates applicable
%int = 5/100; % interest rate that affects all costs
%infl = 3/100; % inflation rate that affects salvage costs
%inc = 4/100; % non-inflation rate at which non-salvage costs increase
%————————————————————————-%
% Solar specifications
cap_pv = 300/1.63; % capital cost of PV module (184.0491 UK pounds/m2)
oandm_pv = 7.5/1.63; % o & m cost of PV module (4.6012 UK pounds/m2/yr)
sal_pv = 60/1.63; % salvage revenue of PV module (36.8098 UK pounds/m2)
%life_pv = 25; % lifetime of PV module (years)
%rep_pv = (proj_life / life_pv) – 1; % number of replacements in project (0)
%————————————————————————-%
% Wind specifications
cap_wt = 1125/(pi*(0.85344.^2)); % capital cost of turbine
% (491.6507 UK pounds/m2)
oandm_wt = 168.75/(pi*(0.85344.^2)); % o & m cost of turbine
% (73.7476 UK pounds/m2/yr)
sal_wt = 225/(pi*(0.85344.^2)); % salvage revenue of turbine
% (98.3301 UK pounds/m2)
%life_wt = 12.5; % lifetime of turbine (years)
%rep_wt = (proj_life / life_wt) – 1; % number of replacements in project (1)
%————————————————————————-%
% Battery specifications
cap_batt = 364/1.68; % capital cost of battery (216.6667 UK pounds/kWh)
oandm_batt = 3.64/1.68; % o & m cost of battery (2.1667 UK pounds/kWh/yr)
sal_batt = 36.4/1.68; % salvage revenue of battery (21.6667 UK pounds/kWh)
%life_batt = 2.5; % lifetime of battery (years)
%rep_batt = (proj_life / life_batt) – 1; % number of replacements in project;
% (9)
%————————————————————————-%
% Useful factors for net present value
%fac1 = (1+inc)/(1+int); % 0.9905
%fac2 = (1+infl)/(1+int); % 0.9810
%factor1a = symsum(fac1.^((k-1)*life_wt),k,1,rep_wt);
factor1a = 1; % summation of fac1^(k-1)*life_wt) for turbine replacements
%factor1b = symsum(fac1.^((k-1)*life_batt),k,1,rep_batt);
factor1b = 8.1943; % summation of fac1.^((k-1)*life_batt) for battery
% replacements
%factor2 = symsum(fac1.^k,k,1,proj_life);
factor2 = 22.1282; % summation of fac1^k for project life
% factor2 = fac1 + (fac1.^2) + (fac1.^3) + … + (fac1.^25)
%factor3a = ((1+infl).^proj_life)/((1+int).^proj_life);
factor3a = 0.6183;
%factor3b = symsum(fac2.^(x*life_wt),x,1,rep_wt);
factor3b = 0.7863; % summation of fac2^(x*life_wt) for turbine life
%factor3c = symsum(fac2.^(x*life_batt),x,1,rep_batt);
factor3c = 7.1315; % summation of fac2^(x*life_batt) for battery life
%————————————————————————-%
% Capital costs and replacement costs
% Solar
pv_caprep = cap_pv * Areapv;
pv_caprep_npv = pv_caprep;
const_pv1 = pv_caprep_npv / Areapv;
% Wind
windg_caprep = cap_wt * Areawt;
windg_caprep_npv = windg_caprep * factor1a;
const_wt1 = windg_caprep_npv / Areawt;
% Battery
batt_caprep = cap_batt * Capbatt;
batt_caprep_npv = batt_caprep * factor1b;
const_batt1 = batt_caprep_npv / Capbatt;
%————————————————————————-%
% Operation and maintenance costs
% Solar
pv_oandm = oandm_pv * Areapv;
pv_oandm_npv = pv_oandm * factor2;
const_pv2 = pv_oandm_npv / Areapv;
% Wind
windg_oandm = oandm_wt * Areawt;
windg_oandm_npv = windg_oandm * factor2;
const_wt2 = windg_oandm_npv / Areawt;
% Battery
batt_oandm = oandm_batt * Capbatt;
batt_oandm_npv = batt_oandm * factor2;
const_batt2 = batt_oandm_npv / Capbatt;
%————————————————————————-%
% Salvage revenues
% Solar
pv_sal = sal_pv * Areapv;
pv_sal_npv = pv_sal * factor3a;
const_pv3 = pv_sal_npv / Areapv;
% Wind
windg_sal = sal_wt * Areawt;
windg_sal_npv = windg_sal * factor3b;
const_wt3 = windg_sal_npv / Areawt;
% Battery
batt_sal = sal_batt * Capbatt;
batt_sal_npv = batt_sal * factor3c;
const_batt3 = batt_sal_npv / Capbatt;
%————————————————————————-%
% Total system cost
% In general: system cost = capital cost + o&m cost – salvage revenue
system_cost = ((const_pv1 + const_pv2 – const_pv3) * Areapv) + …
((const_wt1 + const_wt2 – const_wt3) * Areawt) +((const_batt1 + const_batt2 – const_batt3) * Capbatt);
end
the ga script
nvars = 3; % number of input variables
fun = @cost; % objective function to be optimized (system cost)
lb = [16.3 6.865 16.8]; % lower bounds of input variables
ub = [32.6 22.882 33.6]; % lower bounds of input variables
nonlcon = @LPSP; % nonlinear constraint function (loss of power supply
% probability
% Optimization command
[X,fval] = ga(fun,nvars,[],[],[],[],lb,ub,nonlcon);
i can provide the exel data files you need to run the function

these are the errors i could not fix :
Error using .*
Matrix dimensions must agree.

Error in solar (line 12)
solargen = w .* x;

Error in LPSP (line 9)
s = solar(Areapv); % call solar function

Error in createAnonymousFcn>@(x)fcn(x,FcnArgs{:}) (line 11)
fcn_handle = @(x) fcn(x,FcnArgs{:});

Error in constrValidate (line 23)
[cineq,ceq] = nonlcon(Iterate.x’);

Error in gacommon (line 132)
[LinearConstr, Iterate,nineqcstr,neqcstr,ncstr] = constrValidate(NonconFcn, …

Error in ga (line 336)
NonconFcn,options,Iterate,type] = gacommon(nvars,fun,Aineq,bineq,Aeq,beq,lb,ub, …

Error in gen (line 28)
[X,fval] = ga(fun,nvars,[],[],[],[],lb,ub,nonlcon);

Caused by:
Failure in initial user-supplied nonlinear constraint function evaluation. genetic algorithm MATLAB Answers — New Questions

​

whenever matlab generates a code it assigns unique code for each subsystem and its used for classification of parameters from that particular subsytem , here the problem is everytime the user uses different version of matlab ,in the subsystem this code numbers gets changed and there is lot of manual work involved in mapping these parameters used in hand written codes for interfaces , hence I wanted to check if there is any configuration settings available in the simulink to keep the unique subsystem code representations same irrespective of code gen in any version of matlab/simulink ??whenever matlab generates a code it assigns unique code for each subsystem and its used for classification of parameters from that particular subsytem , here the problem is everytime the user uses different version of matlab ,in the subsystem this code numbers gets changed and there is lot of manual work involved in mapping these parameters used in hand written codes for interfaces , hence I wanted to check if there is any configuration settings available in the simulink to keep the unique subsystem code representations same irrespective of code gen in any version of matlab/simulink ?? whenever matlab generates a code it assigns unique code for each subsystem and its used for classification of parameters from that particular subsytem , here the problem is everytime the user uses different version of matlab ,in the subsystem this code numbers gets changed and there is lot of manual work involved in mapping these parameters used in hand written codes for interfaces , hence I wanted to check if there is any configuration settings available in the simulink to keep the unique subsystem code representations same irrespective of code gen in any version of matlab/simulink ?? simulink generated code MATLAB Answers — New Questions

​

The simulation time set by reinforcement learning is inconsistent with the training time。The simulation time set by reinforcement learning is inconsistent with the training time。 The simulation time set by reinforcement learning is inconsistent with the training time。 reinforcement learning MATLAB Answers — New Questions

​

I am trying to train a CNN model for a classification task. The input to the model is a `1x1024x2` frame and I have the following network:

layers =
28×1 Layer array with layers:

1 ‘Input Layer’ Image Input 1×1024×2 images
2 ‘CNN1’ 2-D Convolution 16 1×8 convolutions with stride [1 1] and padding ‘same’
3 ‘BN1’ Batch Normalization Batch normalization
4 ‘ReLU1’ ReLU ReLU
5 ‘MaxPool1’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
6 ‘CNN2’ 2-D Convolution 24 1×8 convolutions with stride [1 1] and padding ‘same’
7 ‘BN2’ Batch Normalization Batch normalization
8 ‘ReLU2’ ReLU ReLU
9 ‘MaxPool2’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
10 ‘CNN3’ 2-D Convolution 32 1×8 convolutions with stride [1 1] and padding ‘same’
11 ‘BN3’ Batch Normalization Batch normalization
12 ‘ReLU3’ ReLU ReLU
13 ‘MaxPool3’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
14 ‘CNN4’ 2-D Convolution 48 1×8 convolutions with stride [1 1] and padding ‘same’
15 ‘BN4’ Batch Normalization Batch normalization
16 ‘ReLU4’ ReLU ReLU
17 ‘MaxPool4’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
18 ‘CNN5’ 2-D Convolution 64 1×8 convolutions with stride [1 1] and padding ‘same’
19 ‘BN5’ Batch Normalization Batch normalization
20 ‘ReLU5’ ReLU ReLU
21 ‘MaxPool5’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
22 ‘CNN6’ 2-D Convolution 96 1×8 convolutions with stride [1 1] and padding ‘same’
23 ‘BN6’ Batch Normalization Batch normalization
24 ‘ReLU6’ ReLU ReLU
25 ‘AP1’ 2-D Average Pooling 1×32 average pooling with stride [1 1] and padding [0 0 0 0]
26 ‘FC1’ Fully Connected 24 fully connected layer
27 ‘SoftMax’ Softmax softmax
28 ‘Output’ Classification Output crossentropyex

I’m using a custom minibatch dataset to train the model. The read funtion returns the following table structure:

i’m using these training options:
% Specify training options
options = trainingOptions(‘sgdm’, …
‘MiniBatchSize’, miniBatchSize, …
‘MaxEpochs’, 10, …
‘Verbose’, true, …
‘Plots’, ‘training-progress’);

% Train the network
net = trainNetwork(mbds, layers, options);
and i get the following error:
Error using trainNetwork
Operands to the logical AND (&&) and OR (||) operators must be convertible to logical scalar values. Use the ANY or ALL functions to reduce operands to logical scalar values.

Caused by:
Operands to the logical AND (&&) and OR (||) operators must be convertible to logical scalar values. Use the ANY or ALL functions to reduce operands to logical scalar values.
I’ve been stuck on this for a while now, and theres no way to inspect the trainNetwork code to properly debug. I’d appreciate your suggestions. Thank you.

Paul Osinowo
Graduate Student
University of StrathclydeI am trying to train a CNN model for a classification task. The input to the model is a `1x1024x2` frame and I have the following network:

layers =
28×1 Layer array with layers:

1 ‘Input Layer’ Image Input 1×1024×2 images
2 ‘CNN1’ 2-D Convolution 16 1×8 convolutions with stride [1 1] and padding ‘same’
3 ‘BN1’ Batch Normalization Batch normalization
4 ‘ReLU1’ ReLU ReLU
5 ‘MaxPool1’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
6 ‘CNN2’ 2-D Convolution 24 1×8 convolutions with stride [1 1] and padding ‘same’
7 ‘BN2’ Batch Normalization Batch normalization
8 ‘ReLU2’ ReLU ReLU
9 ‘MaxPool2’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
10 ‘CNN3’ 2-D Convolution 32 1×8 convolutions with stride [1 1] and padding ‘same’
11 ‘BN3’ Batch Normalization Batch normalization
12 ‘ReLU3’ ReLU ReLU
13 ‘MaxPool3’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
14 ‘CNN4’ 2-D Convolution 48 1×8 convolutions with stride [1 1] and padding ‘same’
15 ‘BN4’ Batch Normalization Batch normalization
16 ‘ReLU4’ ReLU ReLU
17 ‘MaxPool4’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
18 ‘CNN5’ 2-D Convolution 64 1×8 convolutions with stride [1 1] and padding ‘same’
19 ‘BN5’ Batch Normalization Batch normalization
20 ‘ReLU5’ ReLU ReLU
21 ‘MaxPool5’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
22 ‘CNN6’ 2-D Convolution 96 1×8 convolutions with stride [1 1] and padding ‘same’
23 ‘BN6’ Batch Normalization Batch normalization
24 ‘ReLU6’ ReLU ReLU
25 ‘AP1’ 2-D Average Pooling 1×32 average pooling with stride [1 1] and padding [0 0 0 0]
26 ‘FC1’ Fully Connected 24 fully connected layer
27 ‘SoftMax’ Softmax softmax
28 ‘Output’ Classification Output crossentropyex

I’m using a custom minibatch dataset to train the model. The read funtion returns the following table structure:

i’m using these training options:
% Specify training options
options = trainingOptions(‘sgdm’, …
‘MiniBatchSize’, miniBatchSize, …
‘MaxEpochs’, 10, …
‘Verbose’, true, …
‘Plots’, ‘training-progress’);

% Train the network
net = trainNetwork(mbds, layers, options);
and i get the following error:
Error using trainNetwork
Operands to the logical AND (&&) and OR (||) operators must be convertible to logical scalar values. Use the ANY or ALL functions to reduce operands to logical scalar values.

Caused by:
Operands to the logical AND (&&) and OR (||) operators must be convertible to logical scalar values. Use the ANY or ALL functions to reduce operands to logical scalar values.
I’ve been stuck on this for a while now, and theres no way to inspect the trainNetwork code to properly debug. I’d appreciate your suggestions. Thank you.

Paul Osinowo
Graduate Student
University of Strathclyde I am trying to train a CNN model for a classification task. The input to the model is a `1x1024x2` frame and I have the following network:

layers =
28×1 Layer array with layers:

1 ‘Input Layer’ Image Input 1×1024×2 images
2 ‘CNN1’ 2-D Convolution 16 1×8 convolutions with stride [1 1] and padding ‘same’
3 ‘BN1’ Batch Normalization Batch normalization
4 ‘ReLU1’ ReLU ReLU
5 ‘MaxPool1’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
6 ‘CNN2’ 2-D Convolution 24 1×8 convolutions with stride [1 1] and padding ‘same’
7 ‘BN2’ Batch Normalization Batch normalization
8 ‘ReLU2’ ReLU ReLU
9 ‘MaxPool2’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
10 ‘CNN3’ 2-D Convolution 32 1×8 convolutions with stride [1 1] and padding ‘same’
11 ‘BN3’ Batch Normalization Batch normalization
12 ‘ReLU3’ ReLU ReLU
13 ‘MaxPool3’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
14 ‘CNN4’ 2-D Convolution 48 1×8 convolutions with stride [1 1] and padding ‘same’
15 ‘BN4’ Batch Normalization Batch normalization
16 ‘ReLU4’ ReLU ReLU
17 ‘MaxPool4’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
18 ‘CNN5’ 2-D Convolution 64 1×8 convolutions with stride [1 1] and padding ‘same’
19 ‘BN5’ Batch Normalization Batch normalization
20 ‘ReLU5’ ReLU ReLU
21 ‘MaxPool5’ 2-D Max Pooling 1×2 max pooling with stride [1 2] and padding [0 0 0 0]
22 ‘CNN6’ 2-D Convolution 96 1×8 convolutions with stride [1 1] and padding ‘same’
23 ‘BN6’ Batch Normalization Batch normalization
24 ‘ReLU6’ ReLU ReLU
25 ‘AP1’ 2-D Average Pooling 1×32 average pooling with stride [1 1] and padding [0 0 0 0]
26 ‘FC1’ Fully Connected 24 fully connected layer
27 ‘SoftMax’ Softmax softmax
28 ‘Output’ Classification Output crossentropyex

I’m using a custom minibatch dataset to train the model. The read funtion returns the following table structure:

i’m using these training options:
% Specify training options
options = trainingOptions(‘sgdm’, …
‘MiniBatchSize’, miniBatchSize, …
‘MaxEpochs’, 10, …
‘Verbose’, true, …
‘Plots’, ‘training-progress’);

% Train the network
net = trainNetwork(mbds, layers, options);
and i get the following error:
Error using trainNetwork
Operands to the logical AND (&&) and OR (||) operators must be convertible to logical scalar values. Use the ANY or ALL functions to reduce operands to logical scalar values.

Caused by:
Operands to the logical AND (&&) and OR (||) operators must be convertible to logical scalar values. Use the ANY or ALL functions to reduce operands to logical scalar values.
I’ve been stuck on this for a while now, and theres no way to inspect the trainNetwork code to properly debug. I’d appreciate your suggestions. Thank you.

Paul Osinowo
Graduate Student
University of Strathclyde deep learning, cnn MATLAB Answers — New Questions

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How can I check the coornates info of the image labeler below?How can I check the coornates info of the image labeler below? How can I check the coornates info of the image labeler below? matlab, machine learning, image labeler MATLAB Answers — New Questions

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Hey All,
I’m trying to plot various graphs of the following data but I have no clue where to start. I’ve tried something like the following type of code.
x=zeros(1,10);
x(2:4) =1;
y= linspace(0,10,10);
plot(y,x)
But also tried:
plot(filledData(:,1,filledData(:,2))

I apologize if this is too much, complete beginner. Second day at it.

Thank you all,
JJHey All,
I’m trying to plot various graphs of the following data but I have no clue where to start. I’ve tried something like the following type of code.
x=zeros(1,10);
x(2:4) =1;
y= linspace(0,10,10);
plot(y,x)
But also tried:
plot(filledData(:,1,filledData(:,2))

I apologize if this is too much, complete beginner. Second day at it.

Thank you all,
JJ Hey All,
I’m trying to plot various graphs of the following data but I have no clue where to start. I’ve tried something like the following type of code.
x=zeros(1,10);
x(2:4) =1;
y= linspace(0,10,10);
plot(y,x)
But also tried:
plot(filledData(:,1,filledData(:,2))

I apologize if this is too much, complete beginner. Second day at it.

Thank you all,
JJ double, data, plot, plotting MATLAB Answers — New Questions

​