C++ Neural Networks and Fuzzy Logic: Preface

C++ Neural Networks and Fuzzy Logic

by Valluru B. Rao

MTBooks, IDG Books Worldwide, Inc.

ISBN: 1558515526   Pub Date: 06/01/95

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Table 5.3 lists the vertices and their coordinates, together with a flag that indicates to which subset in the

partitioning the vertex belongs. Note that you can think of the action of the Multilayer Perceptron as that of

evaluating the intersection and union of linearly separable subsets.

Table 5.3 Partitioning of Vertices of a Cube

VertexCoordinatesSubset

O(0,0,0)1

A(0,0,1)1

B(0,1,0)2

C(0,1,1)2

D(1,0,0)2

E(1,0,1)2

F(1,1,0)3

G(1,1,1)3

The network, which is a two−layer Perceptron, meaning two layers of weights, has three neurons in the first

layer and one output neuron in the second layer. Remember that we are counting those layers in which the

neurons do the aggregation of the signals coming into them using the connection weights. The first layer with

the three neurons is what is generally described as the hidden layer, since the second layer is not hidden and is

at the extreme right in the layout of the neural network. Table 5.4 gives an example of the weights you can use

for the connections between the input neurons and the hidden layer neurons. There are three input neurons,

one for each coordinate of the vertex of the cube.

Table 5.4 Weights for Connections Between Input Neurons and Hidden Layer Neurons.