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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Assume that at a particular point in time, LevelSense = 7.0 and TempSense = 65. These are the crisp inputs

directly from the sensors. With fuzzification (refer to Chapter 3 for a review), assume that you get the

following fuzzy memberships:

crisp input — LevelSense = 7.0

fuzzy outputs with membership values −

Medium: 0.4

Large: 0.6

all others : 0.0

crisp input — TempSense=65

fuzzy outputs with membership values −

Medium: 0.75

Large: 0.25

all others: 0.0

This results in four rules firing:

1.  TempSense = Medium (0.75) AND LevelSense = Medium (0.4)

2.  TempSense = Large (0.25) AND LevelSense = Medium (0.4)

3.  TempSense = Medium (0.75) AND LevelSense = Large (0.6)

4.  TempSense = Large (0.25) AND LevelSense = Large (0.6)

First you must determine, for each of the AND clauses in the IF–THEN rules, what the output should be. This

is done by the conjunction or minimum operator. So for each of these rules you have the following firing

strengths: