Neural Network Toolbox

Custom Network

Before you can build a network you need to know what it looks like. For dramatic purposes (and to give the toolbox a workout) this section leads you through the creation of the wild and complicated network shown below.

Each of the two elements of the first network input is to accept values ranging between 0 and 10. Each of the five elements of the second network input ranges from -2 to 2.

Before you can complete your design of this network, the algorithms it employs for initialization and training must be specified.

We agree here that each layer's weights and biases are initialized with the Nguyen-Widrow layer initialization method (initnw). Also, the network is trained with the Levenberg-Marquardt backpropagation (trainlm), so that, given example input vectors, the outputs of the third layer learn to match the associated target vectors with minimal mean squared error (mse).

Custom Networks

Network Definition

© 1994-2005 The MathWorks, Inc.