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cascadeforwardnet

Generate cascade-forward neural network

Description

example

net = cascadeforwardnet(hiddenSizes,trainFcn) returns a cascade-forward neural network with a hidden layer size of hiddenSizes and training function, specified by trainFcn.

Cascade-forward networks are similar to feed-forward networks, but include a connection from the input and every previous layer to following layers.

As with feed-forward networks, a two-or more layer cascade-network can learn any finite input-output relationship arbitrarily well given enough hidden neurons.

Examples

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This example shows how to use a cascade-forward neural network to solve a simple problem.

Load the training data.

[x,t] = simplefit_dataset;

The 1-by-94 matrix x contains the input values and the 1-by-94 matrix t contains the associated target output values.

Construct a cascade-forward network with one hidden layer of size 10.

net = cascadeforwardnet(10);

Train the network net using the training data.

net = train(net,x,t);

View the trained network.

view(net)

Estimate the targets using the trained network.

y = net(x);

Assess the performance of the trained network. The default performance function is mean squared error.

perf = perform(net,y,t)
perf = 1.9372e-05

Input Arguments

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Size of the hidden layers in the network, specified as a row vector. The length of the vector determines the number of hidden layers in the network.

Example: For example, you can specify a network with 3 hidden layers, where the first hidden layer size is 10, the second is 8, and the third is 5 as follows: [10,8,5]

The input and output sizes are set to zero. The software adjusts the sizes of these during training according to the training data.

Data Types: single | double

Training function name, specified as one of the following.

Training FunctionAlgorithm
'trainlm'

Levenberg-Marquardt

'trainbr'

Bayesian Regularization

'trainbfg'

BFGS Quasi-Newton

'trainrp'

Resilient Backpropagation

'trainscg'

Scaled Conjugate Gradient

'traincgb'

Conjugate Gradient with Powell/Beale Restarts

'traincgf'

Fletcher-Powell Conjugate Gradient

'traincgp'

Polak-Ribiére Conjugate Gradient

'trainoss'

One Step Secant

'traingdx'

Variable Learning Rate Gradient Descent

'traingdm'

Gradient Descent with Momentum

'traingd'

Gradient Descent

Example: For example, you can specify the variable learning rate gradient descent algorithm as the training algorithm as follows: 'traingdx'

For more information on the training functions, see Train and Apply Multilayer Shallow Neural Networks and Choose a Multilayer Neural Network Training Function.

Data Types: char

Output Arguments

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Cascade-forward neural network, returned as a network object.

Introduced in R2010b