Читать книгу Introduction to Fuzzy Logic - James K. Peckol - Страница 72

We began this chapter with a look at some of the early works in learning and reasoning. We have seen that vagueness and imprecision are common in everyday life. Very often, the kind of information we encounter may be placed into two major categories: statistical and nonstatistical. The former we model using probabilistic methods, and the latter we model using fuzzy methods.

We introduced and examined the concepts of monotonic, non‐monotonic, and approximate reasoning and several models of human reasoning and learning.

We examined the concepts of crisp and fuzzy subsets and crisp and fuzzy subset membership. We learned that the possible degree of membership μ_xF() (membership of the variable x in the set F) of a variable x in a fuzzy subset spans the range [0.0–1.0] and that when we restrict the membership function so as to admit only two values, 0 and 1, fuzzy subsets reduce to classical sets. We also introduced the membership graph as a tool for expressing membership functions.