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
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,
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