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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Fuzziness in Neural Networks

Fuzziness can enter neural networks to define the weights from fuzzy sets. A comparison between expert

systems and fuzzy systems is important to understand in the context of neural networks. Expert systems are

based on crisp rules. Such crisp rules may not always be available. Expert systems have to consider an

exhaustive set of possibilities. Such sets may not be known beforehand. When crisp rules are not possible, and

when it is not known if the possibilities are exhaustive, the expert systems approach is not a good one.

Some neural networks, through the features of training and learning, can function in the presence of

unexpected situations. Therein neural networks have an advantage over expert systems, and they can manage

with far less information than expert systems need.

One form of fuzziness in neural networks is called a fuzzy cognitive map. A fuzzy cognitive map is like a

dynamic state machine with fuzzy states. A traditional state machine is a machine with defined states and

outputs associated with each state. Transitions from state to state take place according to input events or

stimuli. A fuzzy cognitive map looks like a state machine but has fuzzy states (not just 1 or 0). You have a set

of weights along each transition path, and these weights can be learned from a set of training data.

Our treatment of fuzziness in neural networks is with the discussion of the fuzzy associative memory,

abbreviated as FAM, which, like the fuzzy cognitive map, was developed by Bart Kosko. The FAM and the

C++ implementation are discussed in Chapter 9.