C++ Neural Networks and Fuzzy Logic: Preface

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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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C++ Neural Networks and Fuzzy Logic:Preface

Section I: A Look at Fuzzy Databases and Quantification

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