Fuzzy Sets in Approximate Reasoning and Information Systems by J.C. Bezdek, Didier Dubois, Henri Prade

By J.C. Bezdek, Didier Dubois, Henri Prade

Approximate reasoning is a key motivation in fuzzy units and threat idea. This quantity presents a coherent view of this box, and its effect on database learn and knowledge retrieval. First, the semantic foundations of approximate reasoning are offered. detailed emphasis is given to the illustration of fuzzy ideas and really good forms of approximate reasoning. Then syntactic elements of approximate reasoning are surveyed and the algebraic underpinnings of fuzzy end result kin are awarded and defined. the second one a part of the publication is dedicated to inductive and neuro-fuzzy tools for studying fuzzy principles. It additionally includes new fabric at the software of hazard concept to facts fusion. The final a part of the e-book surveys the becoming literature on fuzzy info structures. every one bankruptcy comprises large bibliographical fabric.
Fuzzy units in Approximate Reasoning and knowledge Systems is a tremendous resource of data for learn students and graduate scholars in computing device technology and synthetic intelligence, attracted to human info processing.

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Note that while llB(A) is symmetrical in both arguments since it expresses to what extent A II B (defined pointwisely as min(A(·) ,B(·» ) is not empty, NB(A) is not so, since it expresses a degree of inclusion of B into A. The set-functions llB and NB are still max and min-decomposable respectively on fuzzy events. However, max(llB(A), I - NB(A) is no longer equal to 1 when A is fuzzy, since we may have llB(A) < I and llB(AC) < 1. Fortunately, llB(A) ~ NB(A) is always satisfied for A and B normalized.

Some authors have considered interval-valued operations. This comes from noticing that normal conjunctive forms and normal disjunctive forms do not coincide in multiple-valued logic. Turksen (1986) uses these forms to compute I(a, b) as an interval. The problem is to make sure that the interval is sufficiently narrow to be informative and sufficiently wide to encompass many implications. Results by Turksen and Yao (1984), Turksen (1986, 1989) indicate that this is the case to some extent. In the case of implication, the disjunctive (DNF) and conjunctive (CNF) normal forms are as follows CNF(p ~ q) = ""p v q DNF(p ~ q) =(p /\ q) v (""p /\ q) v (""p /\ ""q) FUZZY SETS IN APPROXIMATE AND PLAUSmLE REASONING 31 that lead to the following many-valued counterparts for the implication: an S-implication for CNF(a -t b) DNF(a -t b) =S(T(a, b), T(n(a), b), T(n(a), n(b))).

Dubois and Prade (l984a) have shown that S-implications and R-implications could be merged into a single family, provided that the class of triangular norms is enlarged to non-commutative conjunction operators. For instance, Kleene-Dienes implication I(a, b) = max(l - a, b) can be obtained by residuation from the non-commutative conjunction T*(a b) = {b if a + ~ > 1 , 0 otherWIse. This non-commutative conjunction can be obtained from GOdel's iinplication and conversely as T*(a, b) = n(a ~ neb)) and a ~ b = n(T*(a, neb)) respectively.

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