Neural Network Activation function

net = Neural Network name: 'Feed-Forward Neural Network' userdata: (your custom info) dimensions: numInputs: 1 numLayers: 3 numOutputs: 1 numInputDelays: 0 numLayerDelays: 0 numFeedbackDelays: 0 numWeightElements: 230 sampleTime: 1 connections: biasConnect: [1; 1; 1] inputConnect: [1; 0; 0] layerConnect: [0 0 0; 1 0 0; 0 1 0] outputConnect: [0 0 1] subobjects: input: Equivalent to inputs{1} output: Equivalent to outputs{3} inputs: {1x1 cell array of 1 input} layers: {3x1 cell array of 3 layers} outputs: {1x3 cell array of 1 output} biases: {3x1 cell array of 3 biases} inputWeights: {3x1 cell array of 1 weight} layerWeights: {3x3 cell array of 2 weights} functions: adaptFcn: 'adaptwb' adaptParam: (none) derivFcn: 'defaultderiv' divideFcn: 'dividerand' divideParam: .trainRatio, .valRatio, .testRatio divideMode: 'sample' initFcn: 'initlay' performFcn: 'mse' performParam: .regularization, .normalization plotFcns: {'plotperform', 'plottrainstate', 'ploterrhist', 'plotregression'} plotParams: {1x4 cell array of 4 params} trainFcn: 'trainlm' trainParam: .showWindow, .showCommandLine, .show, .epochs, .time, .goal, .min_grad, .max_fail, .mu, .mu_dec, .mu_inc, .mu_max weight and bias values: IW: {3x1 cell} containing 1 input weight matrix LW: {3x3 cell} containing 2 layer weight matrices b: {3x1 cell} containing 3 bias vectors methods: adapt: Learn while in continuous use configure: Configure inputs & outputs gensim: Generate Simulink model init: Initialize weights & biases perform: Calculate performance sim: Evaluate network outputs given inputs train: Train network with examples view: View diagram unconfigure: Unconfigure inputs & outputs