Hi all,
I have some doubts about Neural State-Space Models.
1) I would like to train such a model using different observations (or tests). In other words, I have time sequences measured in different experiments and I would like to use them to train the model. From the examples currently online, I could not figure out how to do this, since it seems to me that the training data are always represented by a single time sequence.
2) Suppose the time sequences I want to train the model with have a sampling time given as d_t. I want to use the Neural State-Space Model that I identify with this data in Simulink. I can do this using just the dedicated Neural State-Space Model block. Suppose, however, that I want to run simulations using this model in Simulink with sample times to my preference. I am aware of rate transition blocks, but can they do the trick for me for this type of problem? In other words, does the Simulink block of the Neural State-Space Model allow me to work with other sampling times, or am I constrained to use the model with sampling times equal to that of the time series with which I have trained the model (thus what has been referred to as d_t)?
Thank you in advance for the support!
Best regards,

MarcoHi all,
I have some doubts about Neural State-Space Models.
1) I would like to train such a model using different observations (or tests). In other words, I have time sequences measured in different experiments and I would like to use them to train the model. From the examples currently online, I could not figure out how to do this, since it seems to me that the training data are always represented by a single time sequence.
2) Suppose the time sequences I want to train the model with have a sampling time given as d_t. I want to use the Neural State-Space Model that I identify with this data in Simulink. I can do this using just the dedicated Neural State-Space Model block. Suppose, however, that I want to run simulations using this model in Simulink with sample times to my preference. I am aware of rate transition blocks, but can they do the trick for me for this type of problem? In other words, does the Simulink block of the Neural State-Space Model allow me to work with other sampling times, or am I constrained to use the model with sampling times equal to that of the time series with which I have trained the model (thus what has been referred to as d_t)?
Thank you in advance for the support!
Best regards,

Marco Hi all,
I have some doubts about Neural State-Space Models.
1) I would like to train such a model using different observations (or tests). In other words, I have time sequences measured in different experiments and I would like to use them to train the model. From the examples currently online, I could not figure out how to do this, since it seems to me that the training data are always represented by a single time sequence.
2) Suppose the time sequences I want to train the model with have a sampling time given as d_t. I want to use the Neural State-Space Model that I identify with this data in Simulink. I can do this using just the dedicated Neural State-Space Model block. Suppose, however, that I want to run simulations using this model in Simulink with sample times to my preference. I am aware of rate transition blocks, but can they do the trick for me for this type of problem? In other words, does the Simulink block of the Neural State-Space Model allow me to work with other sampling times, or am I constrained to use the model with sampling times equal to that of the time series with which I have trained the model (thus what has been referred to as d_t)?
Thank you in advance for the support!
Best regards,

Marco neural state-space models, system identification, machine learning, deep learning, reduced order modelling, simulation MATLAB Answers — New Questions

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