C++ Neural Networks and Fuzzy Logic:Applications of Fuzzy Logic


























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C++ Neural Networks and Fuzzy Logic


(Publisher: IDG Books Worldwide, Inc.)

Author(s): Valluru B. Rao

ISBN: 1558515526

Publication Date: 06/01/95










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Chapter 16Applications of Fuzzy Logic

Introduction
Up until now, we have discussed how fuzzy logic could be used in conjunction with neural networks: We looked at a fuzzifier in Chapter 3 that takes crisp input data and creates fuzzy outputs, which then could be used as inputs to a neural network. In chapter 9, we used fuzzy logic to create a special type of associative memory called a FAM (fuzzy associative memory). In this chapter, we focus on applications of fuzzy logic by itself. This chapter starts with an overview of the different types of application areas for fuzzy logic. We then present two application domains of fuzzy logic: fuzzy control systems, and fuzzy databases and quantification. In these sections, we also introduce some more concepts in fuzzy logic theory.

A Fuzzy Universe of Applications
Fuzzy logic is being applied to a wide variety of problems. The most pervasive field of influence is in control systems, with the rapid acceptance of fuzzy logic controllers (FLCs) for machine and process control. There are a number of other areas where fuzzy logic is being applied. Here is a brief list adapted from Yan, et al., with examples in each area:


•  Biological and Medical Sciences Fuzzy logic based diagnosis systems, cancer research, fuzzy logic based manipulation of prosthetic devices, fuzzy logic based analysis of movement disorders, etc.
•  Management and Decision Support Fuzzy logic based factory site selection, fuzzy logic aided military decision making (sounds scary, but remember that the fuzzy in fuzzy logic applies to the imprecision in the data and not in the logic), fuzzy logic based decision making for marketing strategies, etc.
•  Economics and Finance Fuzzy modeling of complex marketing systems, fuzzy logic based trading systems, fuzzy logic based cost-benefit analysis, fuzzy logic based investment evaluation, etc.
•  Environmental Science Fuzzy logic based weather prediction, fuzzy logic based water quality control, etc.
•  Engineering and Computer Science Fuzzy database systems, fuzzy logic based prediction of earthquakes, fuzzy logic based automation of nuclear plant control, fuzzy logic based computer network design, fuzzy logic based evaluation of architectural design, fuzzy logic control systems, etc.
•  Operations Research Fuzzy logic based scheduling and modeling, fuzzy logic based allocation of resources, etc.
•  Pattern Recognition and Classification Fuzzy logic based speech recognition, fuzzy logic based handwriting recognition, fuzzy logic based facial characteristic analysis, fuzzy logic based military command analysis, fuzzy image search, etc.
•  Psychology Fuzzy logic based analysis of human behavior, criminal investigation and prevention based on fuzzy logic reasoning, etc.
•  Reliability and Quality Control Fuzzy logic based failure diagnosis, production line monitoring and inspection, etc.

We will now move to one of the two application domains that we will discuss in depth, Fuzzy Databases. Later in the chapter, we examine the second application domain, Fuzzy Control Systems.

Section I: A Look at Fuzzy Databases and Quantification
In this section, we want to look at some ways in which fuzzy logic may be applied to databases and operations with databases. Standard databases have crisp data sets, and you create unambiguous relations over the data sets. You also make queries that are specific and that do not have any ambiguity. Introducing ambiguity in one or more of these aspects of standard databases leads to ideas of how fuzzy logic can be applied to databases. Such application of fuzzy logic could mean that you get databases that are easier to query and easier to interface to. A fuzzy search, where search criteria are not precisely bounded, may be more appropriate than a crisp search. You can recall any number of occasions when you tend to make ambiguous queries, since you are not certain of what you need. You also tend to make ambiguous queries when a “ball park” value is sufficient for your purposes.
In this section, you will also learn some more concepts in fuzzy logic. You will see these concepts introduced where they arise in the discussion of ideas relating to fuzzy databases. You may at times see somewhat of a digression in the middle of the fuzzy database discussion to fuzzy logic topics. You may skip to the area where the fuzzy database discussion is resumed and refer back to the skipped areas whenever you feel the need to get clarification of a concept.
We will start with an example of a standard database, relations and queries. We then point out some of the ways in which fuzziness can be introduced.



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