…… … keller box method, matlab code MATLAB Answers — New Questions

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Post Content Post Content unable to get output MATLAB Answers — New Questions

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Hi i am looking to try calculate the latitude and longitude of a point on the ground with respect to an aircraft which has a lat lon but obviously at a specific height, a azmiuth and elevation look angle and also range to the point on ground ( all known ) .

[latout, lonout] = reckon(lat, lon, rng, az)

e.g from help section

dist = nm2deg(600) % convert nm distance to degrees

dist =
9.9933

pt1 = reckon(51.5,0,dist,315) % northwest is 315 degrees

pt1 =
57.8999 -13.3507

The reckon function seems to do near enough exactly what i want but does not take in account elevation angle ( i.e assumes both points are on the ground)

Is there any function similar that i can achieve being able to add in an elevation angle to the calculation, or change the reckon function in anyway?

Any help much appreciatedHi i am looking to try calculate the latitude and longitude of a point on the ground with respect to an aircraft which has a lat lon but obviously at a specific height, a azmiuth and elevation look angle and also range to the point on ground ( all known ) .

[latout, lonout] = reckon(lat, lon, rng, az)

e.g from help section

dist = nm2deg(600) % convert nm distance to degrees

dist =
9.9933

pt1 = reckon(51.5,0,dist,315) % northwest is 315 degrees

pt1 =
57.8999 -13.3507

The reckon function seems to do near enough exactly what i want but does not take in account elevation angle ( i.e assumes both points are on the ground)

Is there any function similar that i can achieve being able to add in an elevation angle to the calculation, or change the reckon function in anyway?

Any help much appreciated Hi i am looking to try calculate the latitude and longitude of a point on the ground with respect to an aircraft which has a lat lon but obviously at a specific height, a azmiuth and elevation look angle and also range to the point on ground ( all known ) .

[latout, lonout] = reckon(lat, lon, rng, az)

e.g from help section

dist = nm2deg(600) % convert nm distance to degrees

dist =
9.9933

pt1 = reckon(51.5,0,dist,315) % northwest is 315 degrees

pt1 =
57.8999 -13.3507

The reckon function seems to do near enough exactly what i want but does not take in account elevation angle ( i.e assumes both points are on the ground)

Is there any function similar that i can achieve being able to add in an elevation angle to the calculation, or change the reckon function in anyway?

Any help much appreciated reckon MATLAB Answers — New Questions

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Hi, Simulink supporters team
I want to print my Entire model as pdf by MATLAB script instead of By Print Option in Simulnik Toolstrip. I have tried 2 ways:
1.Using print( ) function but it only print first page of my model
2. Using exportgraphics( ) but it does not support for Simulink model.
So, is there any way to solve this problem?Hi, Simulink supporters team
I want to print my Entire model as pdf by MATLAB script instead of By Print Option in Simulnik Toolstrip. I have tried 2 ways:
1.Using print( ) function but it only print first page of my model
2. Using exportgraphics( ) but it does not support for Simulink model.
So, is there any way to solve this problem? Hi, Simulink supporters team
I want to print my Entire model as pdf by MATLAB script instead of By Print Option in Simulnik Toolstrip. I have tried 2 ways:
1.Using print( ) function but it only print first page of my model
2. Using exportgraphics( ) but it does not support for Simulink model.
So, is there any way to solve this problem? print, model, matlab, command MATLAB Answers — New Questions

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How can I get the ADSR sample points of a audio file…Like:

"Fake" example:

[Amp, SR] = audioread(audio.file);

[a d s r] = get_ADSR(Amp);

% Results: The sample position of the Amp vector:
a = 85 % End of Attack -> Attack start = Amp(0)
d = 300 % End of Decay
s = 2456 % End of Sustain
r = 50000 % End of ReleaseHow can I get the ADSR sample points of a audio file…Like:

"Fake" example:

[Amp, SR] = audioread(audio.file);

[a d s r] = get_ADSR(Amp);

% Results: The sample position of the Amp vector:
a = 85 % End of Attack -> Attack start = Amp(0)
d = 300 % End of Decay
s = 2456 % End of Sustain
r = 50000 % End of Release How can I get the ADSR sample points of a audio file…Like:

"Fake" example:

[Amp, SR] = audioread(audio.file);

[a d s r] = get_ADSR(Amp);

% Results: The sample position of the Amp vector:
a = 85 % End of Attack -> Attack start = Amp(0)
d = 300 % End of Decay
s = 2456 % End of Sustain
r = 50000 % End of Release envelope, adsr, audio, wave MATLAB Answers — New Questions

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I’m now trying to setup the pixhawk 6X using UAV Toolbox Support Package for PX4 Autopilots (ver. 24.12).
I successfully finished the hardware setup.
Next, I created a simple simulink file as below.
I connected the hardware and tried to see the measurement values, but I could not find the values.
(The displayed values are zero.)
Please tell me how to solve this problem.I’m now trying to setup the pixhawk 6X using UAV Toolbox Support Package for PX4 Autopilots (ver. 24.12).
I successfully finished the hardware setup.
Next, I created a simple simulink file as below.
I connected the hardware and tried to see the measurement values, but I could not find the values.
(The displayed values are zero.)
Please tell me how to solve this problem. I’m now trying to setup the pixhawk 6X using UAV Toolbox Support Package for PX4 Autopilots (ver. 24.12).
I successfully finished the hardware setup.
Next, I created a simple simulink file as below.
I connected the hardware and tried to see the measurement values, but I could not find the values.
(The displayed values are zero.)
Please tell me how to solve this problem. pixhawk 6x, matlab, simulink, sensor, uav toolbox support package MATLAB Answers — New Questions

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Hi
I’m studying neural networks and i’m doing a NN with 2 hidden layers and one neuron in the output layer
While I was studying and coding my NN, I faced one doubt.

In the backward step, the math behind this is clear:
to the output layer we have:
, where ⊙ is the hadamard product.
and to hidden layers, we have:

my problem is that, when I code this formulas, I need to change the second equation when I will calculate the the gradient of the first hidden layer (the hidden layer next to the input layer) to match the dimensions of the matrix as follow below:
% Backpropagation
delta_saida = erro_estim.*selecionar_funcao(saida_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’True’);
delta_h2 = (w_out’*delta_saida).*selecionar_funcao(h2_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’True’);
delta_h1 = (w2*delta_h2′)’.*selecionar_funcao(h1_in_estim,ativ_h1,sig_a,tanh_a,tanh_b,’True’);

%update weights and biases
w_out = w_out + learning_rate*delta_saida*h2_out_estim’;
b_out = b_out + learning_rate*delta_saida;

w2 = w2 + learning_rate*(delta_h2’*h1_out_estim)’;
b2 = b2 + learning_rate*sum(delta_h2);

w1 = w1 + learning_rate*delta_h1’*enter_estim;
b1 = b1 + learning_rate*sum(delta_h2);

% I wrote this code partially in portuguese so let me explain a litlle.
%’delta_saida’ is the gradient of the output layer
%delta_h2 is the gradient of the second hidden layer
%delta_h1 is the gradient of the first hidden layer
%w_out,w2 and w1 are the weights of output, second hidden layer and first hidden layer, respectively.
%b_out,b2 and b1 are the biases of output, second hidden layer and first hidden layer, respectively.
% the function selecionar_funcao() is just to calculate the derivative accordingly with the activation function of the layer

%As you can see, I need to change delta_h1 to match the matrix dimensions
It is right to change the formula like i’m doing in my code ? I’m asking it because in my mind, the way that we calculate the gradient of all hidden layers must be the same, but in my case it isn’t true. I will share part of my code here to help anyone to see if i’m doing some mistake
%weights and biases initialization
w1 = randn(num_entradas,n_h1)*sqrt(2/num_entradas);
w2 = randn(n_h1,n_h2) *sqrt(2/n_h1);
w_out = randn(n_h2,n_out) *sqrt(2/n_h2);

b1 = randn(1, n_h1) * sqrt(2/num_entradas);
b2 = randn(1, n_h2) * sqrt(2/n_h1);
b_out = randn(1,n_out) * sqrt(2/n_h2);

%backpropagation
for epoch =1:max_epocas
soma_valid = 0;
soma_estim = 0;
%embaralhar os dados
conj_estim = embaralhar(conj_estim);
% conj_valid = embaralhar(conj_valid);

%Validating
for j=1:size(conj_valid,1)
enter_valid = conj_valid(j,2:end);

h1_in_valid = [enter_valid,1]*[w1;b1];
h1_out_valid = selecionar_funcao(h1_in_valid,ativ_h1,sig_a,tanh_a,tanh_b,’False’);

h2_in_valid = [h1_out_valid,1]*[w2;b2];
h2_out_valid = selecionar_funcao(h2_in_valid,ativ_h2,sig_a,tanh_a,tanh_b,’False’);

saida_in_valid = [h2_out_valid,1]*[w_out;b_out];
saida_out_valid = selecionar_funcao(saida_in_valid,ativ_out,sig_a,tanh_a,tanh_b,’False’);

erro_valid = conj_valid(j,1) – saida_out_valid;

soma_valid = soma_valid + (erro_valid^2);
end
erro_atual_valid = (soma_valid/(2*size(conj_valid,1)));
erros_epoca_valid = [erros_epoca_valid;erro_atual_valid];

%trainning
for i =1:size(conj_estim,1)
enter_estim = conj_estim(i,2:end);

h1_in_estim = [enter_estim,1]*[w1;b1];
h1_out_estim = selecionar_funcao(h1_in_estim,ativ_h1,sig_a,tanh_a,tanh_b,’False’);

h2_in_estim = [h1_out_estim,1]*[w2;b2];
h2_out_estim = selecionar_funcao(h2_in_estim,ativ_h2,sig_a,tanh_a,tanh_b,’False’);

saida_in_estim = [h2_out_estim,1]*[w_out;b_out];
saida_out_estim = selecionar_funcao(saida_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’False’);

erro_estim = conj_estim(i,1) – saida_out_estim;
soma_estim = soma_estim + (erro_estim^2);

% Backpropagation
delta_saida = erro_estim.*selecionar_funcao(saida_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’True’);
delta_h2 = (w_out’*delta_saida).*selecionar_funcao(h2_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’True’);
delta_h1 = (w2*delta_h2′)’.*selecionar_funcao(h1_in_estim,ativ_h1,sig_a,tanh_a,tanh_b,’True’);

%atualizar pesos e biases
w_out = w_out + learning_rate*delta_saida*h2_out_estim’;
b_out = b_out + learning_rate*delta_saida;

w2 = w2 + learning_rate*(delta_h2’*h1_out_estim)’;
b2 = b2 + learning_rate*sum(delta_h2);

w1 = w1 + learning_rate*delta_h1’*enter_estim;
b1 = b1 + learning_rate*sum(delta_h2);

end
erro_atual_estim = (soma_estim/(2*size(conj_estim,1)));
erros_epoca_estim = [erros_epoca_estim;erro_atual_estim];
if erros_epoca_estim(epoch) <limiar
break
else
end
endHi
I’m studying neural networks and i’m doing a NN with 2 hidden layers and one neuron in the output layer
While I was studying and coding my NN, I faced one doubt.

In the backward step, the math behind this is clear:
to the output layer we have:
, where ⊙ is the hadamard product.
and to hidden layers, we have:

my problem is that, when I code this formulas, I need to change the second equation when I will calculate the the gradient of the first hidden layer (the hidden layer next to the input layer) to match the dimensions of the matrix as follow below:
% Backpropagation
delta_saida = erro_estim.*selecionar_funcao(saida_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’True’);
delta_h2 = (w_out’*delta_saida).*selecionar_funcao(h2_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’True’);
delta_h1 = (w2*delta_h2′)’.*selecionar_funcao(h1_in_estim,ativ_h1,sig_a,tanh_a,tanh_b,’True’);

%update weights and biases
w_out = w_out + learning_rate*delta_saida*h2_out_estim’;
b_out = b_out + learning_rate*delta_saida;

w2 = w2 + learning_rate*(delta_h2’*h1_out_estim)’;
b2 = b2 + learning_rate*sum(delta_h2);

w1 = w1 + learning_rate*delta_h1’*enter_estim;
b1 = b1 + learning_rate*sum(delta_h2);

% I wrote this code partially in portuguese so let me explain a litlle.
%’delta_saida’ is the gradient of the output layer
%delta_h2 is the gradient of the second hidden layer
%delta_h1 is the gradient of the first hidden layer
%w_out,w2 and w1 are the weights of output, second hidden layer and first hidden layer, respectively.
%b_out,b2 and b1 are the biases of output, second hidden layer and first hidden layer, respectively.
% the function selecionar_funcao() is just to calculate the derivative accordingly with the activation function of the layer

%As you can see, I need to change delta_h1 to match the matrix dimensions
It is right to change the formula like i’m doing in my code ? I’m asking it because in my mind, the way that we calculate the gradient of all hidden layers must be the same, but in my case it isn’t true. I will share part of my code here to help anyone to see if i’m doing some mistake
%weights and biases initialization
w1 = randn(num_entradas,n_h1)*sqrt(2/num_entradas);
w2 = randn(n_h1,n_h2) *sqrt(2/n_h1);
w_out = randn(n_h2,n_out) *sqrt(2/n_h2);

b1 = randn(1, n_h1) * sqrt(2/num_entradas);
b2 = randn(1, n_h2) * sqrt(2/n_h1);
b_out = randn(1,n_out) * sqrt(2/n_h2);

%backpropagation
for epoch =1:max_epocas
soma_valid = 0;
soma_estim = 0;
%embaralhar os dados
conj_estim = embaralhar(conj_estim);
% conj_valid = embaralhar(conj_valid);

%Validating
for j=1:size(conj_valid,1)
enter_valid = conj_valid(j,2:end);

h1_in_valid = [enter_valid,1]*[w1;b1];
h1_out_valid = selecionar_funcao(h1_in_valid,ativ_h1,sig_a,tanh_a,tanh_b,’False’);

h2_in_valid = [h1_out_valid,1]*[w2;b2];
h2_out_valid = selecionar_funcao(h2_in_valid,ativ_h2,sig_a,tanh_a,tanh_b,’False’);

saida_in_valid = [h2_out_valid,1]*[w_out;b_out];
saida_out_valid = selecionar_funcao(saida_in_valid,ativ_out,sig_a,tanh_a,tanh_b,’False’);

erro_valid = conj_valid(j,1) – saida_out_valid;

soma_valid = soma_valid + (erro_valid^2);
end
erro_atual_valid = (soma_valid/(2*size(conj_valid,1)));
erros_epoca_valid = [erros_epoca_valid;erro_atual_valid];

%trainning
for i =1:size(conj_estim,1)
enter_estim = conj_estim(i,2:end);

h1_in_estim = [enter_estim,1]*[w1;b1];
h1_out_estim = selecionar_funcao(h1_in_estim,ativ_h1,sig_a,tanh_a,tanh_b,’False’);

h2_in_estim = [h1_out_estim,1]*[w2;b2];
h2_out_estim = selecionar_funcao(h2_in_estim,ativ_h2,sig_a,tanh_a,tanh_b,’False’);

saida_in_estim = [h2_out_estim,1]*[w_out;b_out];
saida_out_estim = selecionar_funcao(saida_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’False’);

erro_estim = conj_estim(i,1) – saida_out_estim;
soma_estim = soma_estim + (erro_estim^2);

% Backpropagation
delta_saida = erro_estim.*selecionar_funcao(saida_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’True’);
delta_h2 = (w_out’*delta_saida).*selecionar_funcao(h2_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’True’);
delta_h1 = (w2*delta_h2′)’.*selecionar_funcao(h1_in_estim,ativ_h1,sig_a,tanh_a,tanh_b,’True’);

%atualizar pesos e biases
w_out = w_out + learning_rate*delta_saida*h2_out_estim’;
b_out = b_out + learning_rate*delta_saida;

w2 = w2 + learning_rate*(delta_h2’*h1_out_estim)’;
b2 = b2 + learning_rate*sum(delta_h2);

w1 = w1 + learning_rate*delta_h1’*enter_estim;
b1 = b1 + learning_rate*sum(delta_h2);

end
erro_atual_estim = (soma_estim/(2*size(conj_estim,1)));
erros_epoca_estim = [erros_epoca_estim;erro_atual_estim];
if erros_epoca_estim(epoch) <limiar
break
else
end
end Hi
I’m studying neural networks and i’m doing a NN with 2 hidden layers and one neuron in the output layer
While I was studying and coding my NN, I faced one doubt.

In the backward step, the math behind this is clear:
to the output layer we have:
, where ⊙ is the hadamard product.
and to hidden layers, we have:

my problem is that, when I code this formulas, I need to change the second equation when I will calculate the the gradient of the first hidden layer (the hidden layer next to the input layer) to match the dimensions of the matrix as follow below:
% Backpropagation
delta_saida = erro_estim.*selecionar_funcao(saida_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’True’);
delta_h2 = (w_out’*delta_saida).*selecionar_funcao(h2_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’True’);
delta_h1 = (w2*delta_h2′)’.*selecionar_funcao(h1_in_estim,ativ_h1,sig_a,tanh_a,tanh_b,’True’);

%update weights and biases
w_out = w_out + learning_rate*delta_saida*h2_out_estim’;
b_out = b_out + learning_rate*delta_saida;

w2 = w2 + learning_rate*(delta_h2’*h1_out_estim)’;
b2 = b2 + learning_rate*sum(delta_h2);

w1 = w1 + learning_rate*delta_h1’*enter_estim;
b1 = b1 + learning_rate*sum(delta_h2);

% I wrote this code partially in portuguese so let me explain a litlle.
%’delta_saida’ is the gradient of the output layer
%delta_h2 is the gradient of the second hidden layer
%delta_h1 is the gradient of the first hidden layer
%w_out,w2 and w1 are the weights of output, second hidden layer and first hidden layer, respectively.
%b_out,b2 and b1 are the biases of output, second hidden layer and first hidden layer, respectively.
% the function selecionar_funcao() is just to calculate the derivative accordingly with the activation function of the layer

%As you can see, I need to change delta_h1 to match the matrix dimensions
It is right to change the formula like i’m doing in my code ? I’m asking it because in my mind, the way that we calculate the gradient of all hidden layers must be the same, but in my case it isn’t true. I will share part of my code here to help anyone to see if i’m doing some mistake
%weights and biases initialization
w1 = randn(num_entradas,n_h1)*sqrt(2/num_entradas);
w2 = randn(n_h1,n_h2) *sqrt(2/n_h1);
w_out = randn(n_h2,n_out) *sqrt(2/n_h2);

b1 = randn(1, n_h1) * sqrt(2/num_entradas);
b2 = randn(1, n_h2) * sqrt(2/n_h1);
b_out = randn(1,n_out) * sqrt(2/n_h2);

%backpropagation
for epoch =1:max_epocas
soma_valid = 0;
soma_estim = 0;
%embaralhar os dados
conj_estim = embaralhar(conj_estim);
% conj_valid = embaralhar(conj_valid);

%Validating
for j=1:size(conj_valid,1)
enter_valid = conj_valid(j,2:end);

h1_in_valid = [enter_valid,1]*[w1;b1];
h1_out_valid = selecionar_funcao(h1_in_valid,ativ_h1,sig_a,tanh_a,tanh_b,’False’);

h2_in_valid = [h1_out_valid,1]*[w2;b2];
h2_out_valid = selecionar_funcao(h2_in_valid,ativ_h2,sig_a,tanh_a,tanh_b,’False’);

saida_in_valid = [h2_out_valid,1]*[w_out;b_out];
saida_out_valid = selecionar_funcao(saida_in_valid,ativ_out,sig_a,tanh_a,tanh_b,’False’);

erro_valid = conj_valid(j,1) – saida_out_valid;

soma_valid = soma_valid + (erro_valid^2);
end
erro_atual_valid = (soma_valid/(2*size(conj_valid,1)));
erros_epoca_valid = [erros_epoca_valid;erro_atual_valid];

%trainning
for i =1:size(conj_estim,1)
enter_estim = conj_estim(i,2:end);

h1_in_estim = [enter_estim,1]*[w1;b1];
h1_out_estim = selecionar_funcao(h1_in_estim,ativ_h1,sig_a,tanh_a,tanh_b,’False’);

h2_in_estim = [h1_out_estim,1]*[w2;b2];
h2_out_estim = selecionar_funcao(h2_in_estim,ativ_h2,sig_a,tanh_a,tanh_b,’False’);

saida_in_estim = [h2_out_estim,1]*[w_out;b_out];
saida_out_estim = selecionar_funcao(saida_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’False’);

erro_estim = conj_estim(i,1) – saida_out_estim;
soma_estim = soma_estim + (erro_estim^2);

% Backpropagation
delta_saida = erro_estim.*selecionar_funcao(saida_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’True’);
delta_h2 = (w_out’*delta_saida).*selecionar_funcao(h2_in_estim,ativ_out,sig_a,tanh_a,tanh_b,’True’);
delta_h1 = (w2*delta_h2′)’.*selecionar_funcao(h1_in_estim,ativ_h1,sig_a,tanh_a,tanh_b,’True’);

%atualizar pesos e biases
w_out = w_out + learning_rate*delta_saida*h2_out_estim’;
b_out = b_out + learning_rate*delta_saida;

w2 = w2 + learning_rate*(delta_h2’*h1_out_estim)’;
b2 = b2 + learning_rate*sum(delta_h2);

w1 = w1 + learning_rate*delta_h1’*enter_estim;
b1 = b1 + learning_rate*sum(delta_h2);

end
erro_atual_estim = (soma_estim/(2*size(conj_estim,1)));
erros_epoca_estim = [erros_epoca_estim;erro_atual_estim];
if erros_epoca_estim(epoch) <limiar
break
else
end
end machine learning, backpropagation MATLAB Answers — New Questions

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