Feedforward neural network
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.
Row vector of one or more hidden layer sizes (default = 10)
Training function (default =
and returns a feedforward neural network.
This example shows how to use feedforward neural network 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