.SMArtInt.BaseClasses.BaseRecurrentNeuralNet

Information

Use this base class if you want to include a recurrent neural network in Modelica.

Please extend this model in your own model. After extending you have to

• give the path to the TFLite model
• specify the number of inputs
• specify the sampling interval and how many elements from previous times should be fed into the net
• create input and output connectors
• connect input and output connectors to the single input and single output vector of the runInference submodel

In continuous mode the historic values are generated by a delay. You could choose between the computational improved Clara-Delay and the delay in the Modelica-Standard-Library. If continuous is deactivated the model will create an event when reaching the sampling time - this should be avoided for performance reasons.

As tensorflow lite uses a flattened input array, you have to specify the flattening method. In the standard tensorflow setting you have to use the predefined option OldFirstInputSequential.

For an exemplaric usage the user can take a look at the model TF_PI_RNN which uses the block EvaluateStatefulRecurrentNeuralNet which extends from this base model.