Introduction to MATLAB Neural Network Toolbox

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Presentation transcript:

Introduction to MATLAB Neural Network Toolbox Static Neural Networks Jeen-Shing Wang Yu-Liang Hsu Department of Electrical Engineering National Cheng Kung University October 13, 2014

Basic Concept Step 1: Data collection Create training and testing data u y System Step 1: Data collection Create training and testing data Step 2: Network creation Static networks Dynamic networks Step 3: Network parameter setting No. of hidden layer, neurons, activation function, … Step 4: Learning algorithm selection Backpropagation, … Learning rate Step 5: Learning condition setting Learning epochs, error, … Step 6: Network learning Step 7: Data testing Training Procedure w1 w2 wn … x1 x2 xn Synaptic weights Activation function (Cell body) Axon Output Desired Testing Procedure

Neural Network Models 1. Perceptron 2. Linear Filters 3. Backpropagation 4. Radial Basis Networks 5. Competitive Networks 6. Learning Vector Quantization Network 7. Recurrent Networks 8. NARX Networks

Transfer Function Graphs

Learning Algorithms

Feedforward Neural Networks Scalar: a, b, c; Vector: a, b, c; Matrix: A, B, C; P (r×N): Input data (r: No. of input elements, N: No. of input patterns) Y (m×N): Output data (m: No. of output elements) n: nth layer, including the hidden and output layers IWk, l: Input weight matrix (lth input set to kth layer) LWk, l: Layer weight matrix (lth layer to kth layer)

Feedforward Neural Networks

Feedforward Neural Networks Syntax: newff net = newff(P, T, [S1 … S(n-1)], {TF1 … TFn}, BTF) Si: Size of ith layer, for N-1 layers. (Default = [].) TFi: Transfer function of ith layer. (Default = ‘tansig’ for hidden layers and ‘purelin’ for output layer.) BTF: Backpropagation network training function (Default = ‘trainlm’)

Feedforward Neural Networks Example 1: t = abs(p) Training input data: P = [0 -1 2 -3 4 -5 6 -7 8 -9 10]; Training output data: T = [0 1 2 3 4 5 6 7 8 9 10]; 60% >>clc; clear all; close all; >>% Collect data >>P = [0 -1 2 -3 4 -5 6 -7 8 -9 10]; >>T = [0 1 2 3 4 5 6 7 8 9 10]; >>% Create network >>net = newff(P,T,10); >>net >>% Training >>net = train(net, P, T); >>% Testing >>y = sim(net,P); >>% Error >>error = mse(y-T) 60% 20% 20%

Feedforward Neural Networks

Train Feedforward Neural Networks Set training parameter values: net.trainParam >>net.trainParam.epochs = 100; >>net.trainParam.lr = 0.01; >>net.trainParam.goal = 0; Train the network >>net = train(net, P, T); Simulate or test the network >>y = sim(net, Pt); Testing input data: Pt = [0 1 -2 3 -4 5 -6 7 -8 9 -10]; Testing output data: Tt = [0 1 2 3 4 5 6 7 8 9 10];

Train Feedforward Neural Networks >>net = train(net, P, T,[],[],[],[]);

Graph User Interface (GUI) >>nntool

Pattern Recognition >>Tc = ind2vec(T_for_PNN); >>pnnnet = newpnn(P, Tc); >>Yc = sim(pnnnet, test_letter); >>test_index >>Y = vec2ind(Yc) The training/testing data are available on the course website (http://140.116.215.51)

Thanks for your attention !! Any Question?