C++ Neural Networks and Fuzzy Logic: Preface

Table 5.6 Threshold Values

LayerNeuronThreshold Value

hidden11.8

hidden20.05

hidden3−0.2

output10.5

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C++ Neural Networks and Fuzzy Logic:Preface

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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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Performance of the Perceptron

When you input the coordinates of the vertex G, which has 1 for each coordinate, the first hidden−layer

neuron aggregates these inputs and gets a value of 2.2. Since 2.2 is more than the threshold value of the first

neuron in the hidden layer, that neuron fires, and its output of 1 becomes an input to the output neuron on the

connection with weight 0.6. But you need the activations of the other hidden−layer neurons as well. Let us

describe the performance with coordinates of G as the inputs to the network. Table 5.7 describes this.

Table 5.7 Results with Coordinates of Vertex G as Input

Vertex/