.NeuralNetwork.Networks.NeuralNetwork_ThreeLayer

Information

This block models a three layer Neural Network.

A three layer Neural Network is composed by three NeuralNetworkLayer (FirstHiddenLayer_..., SecondHiddenLayer_... and OutputLayer_...). Everyone is specified by the following parameters:

• numNeurons: it specifies the number of neurons which compose the layer (it is also equal to the rows numer of the weight and bias matrix and to the number of outputs of the layer;
• numInputs: it specifies the number of inputs of the layer (it is also equal to the columns numer of the weight matrix;
• weightTable: it is the weight table of the layer ([Number of Neurons x Number of Inputs]);
• biasTable: it is the bias table of the layer ([Number of Neurons x 1]);
• NeuronActivationFunction: it is the activation function of the layer; it can be equal to:
  • NeuralNetwork.Types.ActivationFunction.TanSig = Hyperbolic tangent sigmoid activation function
  • NeuralNetwork.Types.ActivationFunction.LogSig = Logarithmic sigmoid activation function
  • NeuralNetwork.Types.ActivationFunction.RadBas = Radial basis activation function
  • NeuralNetwork.Types.ActivationFunction.PureLin = Linear activation function

    To get the weight and bias table as modelica wants two different ways can be used:

    • using the extractData.m MatLab script, located in Utilities folder;
    • using the DataFiles Dymola library.

    Release Notes:

    • April 28, 2006 by Fabio Codec?:
      

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