C++ Neural Networks and Fuzzy Logic: Preface

•  Ford Motor Company has developed trainable fuzzy systems for automobile idle−speed control.

•  National Semiconductor Corporation has a software product called NeuFuz that supports the

generation of fuzzy rules with a neural network for control applications.

•  A number of Japanese consumer and industrial products use neural networks with fuzzy systems,

including vacuum cleaners, rice cookers, washing machines, and photocopying machines.

C++ Neural Networks and Fuzzy Logic:Preface

Fuzziness in Neural Networks

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•  AEG Corporation of Germany uses a neural−network−trained fuzzy control system for its water−

and energy−conserving washing machine. After the machine is loaded with laundry, it measures the

water level with a pressure sensor and infers the amount of laundry in the machine by the speed and

volume of water. A total of 157 rules were generated by a neural network that was trained on data

correlating the amount of laundry with the measurement of water level on the sensor.

Summary

In this chapter, you read about fuzzy logic, fuzzy sets, and simple operations on fuzzy sets. Fuzzy logic,

unlike Boolean logic, has more than two on or off categories to describe behavior of systems. You use

membership values for data in fuzzy categories, which may overlap. In this chapter, you also developed a

fuzzifier program in C++ that takes crisp values and converts them to fuzzy values, based on categories and

memberships that you define. For use with neural networks, fuzzy logic can serve as a post−processing or

pre−processing filter. Kosko developed neural networks that use fuzziness and called them fuzzy associative

memories, which will be discussed in later chapters. You also read about how neural networks can be used in

fuzzy systems to define membership functions and fuzzy rules.

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C++ Neural Networks and Fuzzy Logic:Preface

Summary

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