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Jan 14, 2019 at 11:00 comment added Sebastian Oh my... I'm just realizing that I never knew you could input an array in HiddenSize in order to add multiple layers. Well thank you for that, I'll have a lot of thing to try with matlab. I'll also have to check how to change the activation function and/or to use gated cells.
Jan 14, 2019 at 10:42 comment added maksylon mathworks.com/matlabcentral/answers/…. Here is an example how to change net's transfer function. Number of layers and neurons could be manipulated by hiddenSize argument in feedforwardnet(hiddenSize, trainAlgorithm). [10 7 5] for example, means network with 3 hidden layers with 10, 7 and 5 neuron each respective layers. You mentioned you need to take inertia in account. Using that feedback Y(t-d) input could help you with that. I am using it for identification of dynamic systems and it works
Jan 14, 2019 at 10:39 comment added maksylon I am using Neural Networks Toolbox from MATLAB 2017b. Using a GUI doesnt give a freedom in network customization by doing it by scripts/functions do a thing for me. You can pick layers' count, count of neurons in each layers, transfer functions, input signals, training algorithm and so on. mathworks.com/help/deeplearning/ref/feedforwardnet.html Here is a doc with examples how to train feedforward network which might be enough if your model doesn't need to model dynamic properties.
Jan 14, 2019 at 10:19 comment added Sebastian Thank you for your anwser. Yes, making two models seems the best way to obtain best accuracy in my scenario. Regarding NARX approach, however, is that it formulates Y(t) as f(X(t-1), ..., X(t-d), Y(t-1), ..., Y(t-d)) while I'm trying to formulate Y(t) as f( X(t), ..., X(t-d) ). Matlab proposes the Nonlinear Input Output model, which ressembles what I am aiming to do. However, it kinda lack freedom on the network's customization. Maybe I am missing something available in the matlab toolboxes ?
Jan 14, 2019 at 9:30 history answered maksylon CC BY-SA 4.0