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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Recall of Vectors

When X

1

 is presented at the input layer, the activation at the output layer will give ( –4, 4, 2) to which we

apply the thresholding function, which replaces a positive value by 1, and a negative value by 0.

This then gives us the vector (0, 1, 1) as the output, which is the same as our Y

1

. Now Y

1

 is passed back to the

input layer through the feedback connections, and the activation of the input layer becomes the vector (2, –2,

2, 2), which after thresholding gives the output vector (1, 0, 1, 1), same as X

1

. When X

2

 is presented at the

input layer, the activation at the output layer will give (2, –2, 2) to which the thresholding function, which

replaces a positive value by 1 and a negative value by 0, is applied. This then gives the vector (1, 0, 1) as the

output, which is the same as Y