newcf (Neural Network Toolbox)

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newcf

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Trainable cascade-forward backpropagation network

Syntax

net = newcf(Pr,[S1 S2...SNl],{TF1 TF2...TFNl},BTF,BLF,PF)

Description

newcf(PR,[S1 S2...SNl],{TF1 TF2...TFNl},BTF,BLF,PF) takes,

PR - R x 2 matrix of min and max values for R input elements.

Si - Size of ith layer, for Nl layers.

TFi - Transfer function of ith layer, default = 'tansig'.

BTF - Backprop network training function, default = 'traingd'.

BLF - Backprop weight/bias learning function, default = 'learngdm'.

PF - Performance function, default = 'mse'.

and returns an N layer cascade-forward backprop network.

The transfer functions TFi can be any differentiable transfer function such as tansig, logsig, or purelin.

The training function BTF can be any of the backprop training functions such as trainlm, trainbfg, trainrp, traingd, etc.

WARNING: trainlm is the default training function because it is very fast, but it requires a lot of memory to run. If you get an out-of-memory error when training try doing one of these:

1.: Slow trainlm training, but reduce memory requirements by setting net.trainParam.mem_reduc to 2 or more. (See trainlm.)

2.: Use trainbfg, which is slower but more memory-efficient than trainlm.

3.: Use trainrp which is slower but more memory-efficient than trainbfg.

The learning function BLF can be either of the backpropagation learning functions such as learngd, or learngdm.

The performance function can be any of the differentiable performance functions such as mse or msereg.

Examples

Here is a problem consisting of inputs P and targets T that we would like to solve with a network.

P = [0 1 2 3 4 5 6 7 8 9 10];
T = [0 1 2 3 4 3 2 1 2 3 4];

Here a two-layer cascade-forward network is created. The network's input ranges from [0 to 10]. The first layer has five tansig neurons, the second layer has one purelin neuron. The trainlm network training function is to be used.

net = newcf([0 10],[5 1],{'tansig' 'purelin'});

Here the network is simulated and its output plotted against the targets.

Y = sim(net,P);
plot(P,T,P,Y,'o')

Here the network is trained for 50 epochs. Again the network's output is plotted.

net.trainParam.epochs = 50;
net = train(net,P,T);
Y = sim(net,P);
plot(P,T,P,Y,'o')

Algorithm

Cascade-forward networks consist of Nl layers using the dotprod weight function, netsum net input function, and the specified transfer functions.

The first layer has weights coming from the input. Each subsequent layer has weights coming from the input and all previous layers. All layers have biases. The last layer is the network output.

Each layer's weights and biases are initialized with initnw.

Adaption is done with adaptwb which updates weights with the specified learning function. Training is done with the specified training function. Performance is measured according to the specified performance function.