C++ Neural Networks and Fuzzy Logic: Preface

A (it is therefore not listed in A), it is not listed in A[cap]B.

Let us impart fuzzy values (FV) to each of the attributes, age, lov, nov, yov, and hs by defining the sets in

Table 16.2.

Table 16.2 Fuzzy Values for Example Sets

Fuzzy ValueSet

FV(age){ very young, young, somewhat old, old }

FV(nov){ never, rarely, quite a few, often, very often }

FV(lov){ barely few days, few days, quite a few days, many days }

FV(yov){distant past, recent past, recent }

FV(hs){ barely, adequately, quite a bit, extensively }

The attributes of name, citizenship, country of visit are clearly not candidates for having fuzzy values. The

attributes of lps, and lpu, which stand for language in which speaking proficiency and language in which

understanding ability exist, can be coupled into another attribute called flp (foreign language proficiency) with

fuzzy values. We could have introduced in the original list an attribute called lpr ( language with proficiency

to read) along with lps and lpu. As you can see, these three can be taken together into the fuzzy−valued

attribute of foreign language proficiency. We give below the fuzzy values of flp.

   FV(flp) = {not proficient, barely proficient, adequate,

             proficient, very proficient }

Note that each fuzzy value of each attribute gives rise to a fuzzy set, which depends on the elements you

consider for the set and their degrees of membership.

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Fuzzy Scenarios

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