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Feedforward neural network

`feedforwardnet(hiddenSizes,trainFcn)`

Feedforward networks consist of a series of layers. The first layer has a connection from the network input. Each subsequent layer has a connection from the previous layer. The final layer produces the network's output.

Feedforward networks can be used for any kind of input to output mapping. A feedforward network with one hidden layer and enough neurons in the hidden layers, can fit any finite input-output mapping problem.

Specialized versions of the feedforward network include fitting
(`fitnet`) and pattern recognition
(`patternnet`) networks. A variation
on the feedforward network is the cascade forward network (`cascadeforwardnet`) which has additional
connections from the input to every layer, and from each layer to
all following layers.

`feedforwardnet(hiddenSizes,trainFcn)` takes
these arguments,

hiddenSizes | Row vector of one or more hidden layer sizes (default = 10) |

trainFcn | Training function (default = |

and returns a feedforward neural network.

Here a feedforward neural network is used to solve a simple problem.

[x,t] = simplefit_dataset; net = feedforwardnet(10); net = train(net,x,t); view(net) y = net(x); perf = perform(net,y,t)

perf = 1.4639e-04

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