Hi Marco,
Thanks for your questions.
- You can definitely use several different experiments for training neural state space models, but experiments must all be of the same length and all use the same sampling rate. If you have a long experiment, you can split it into multiple experiments. Please take a look at this example, specifically Model Training section. The code in this section includes a segment where training data is prepared by creating multiple experiments that represent overlapping segments. The objects Expts contains multiple experiments.
- For simulation in Simulink, you are probably better off with creating a continuous-time neural state space model. You can do it by simply not specifying sampling time when you create neural state space object using idNeuralStateSpace command. Training continous-time neural state-space model is slower than training discrete-time model, but gives you more flexibility with using the model, such as simulating at different sample times in Simulink. Alternatively, you can train discrete-time model by specifying a sample time in idNeuralStateSpace, but then you would indeed need to use rate converter block in Simulink, and model accuracy may not be optimal espcially when upsampling.
HTH.
Arkadiy
