net = newp([-1 1; -1 1], 1); net.trainParam.epochs = 10; net = train(net, P, T);
Introduction to Neural Networks Using MATLAB 6.0: A Historical and Technical Blueprint introduction to neural networks using matlab 6.0 .pdf
The fundamental building block of any MATLAB network is the single neuron. It ingests an input vector, applies weights, adds a bias, and passes the scalar result through an activation function. n=Wp+bbold n equals bold cap W bold p plus bold b a=f(n)bold a equals f of open paren bold n close paren is the input vector of length Wbold cap W weight matrix ( = number of neurons). is the bias vector. is the net input argument. is the transfer (activation) function. is the output vector. Essential Activation Functions in MATLAB 6.0 net = newp([-1 1; -1 1], 1); net
newrbe (exact design, matching the number of neurons to training samples) and newrb (creates neurons iteratively until an error goal is met). 3. Training Algorithms and Optimization is the bias vector
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net = train(net, P, T); view(net) % Look at the weights
The sim(net, inputs) command is largely replaced by calling the network object directly as a function handle: net(inputs) .