Development of Probabilistic and Possebilistic Approaches to Approximate Reasoning and Its Applications
Abstract
The research has been focused in the main on the development of fuzzy logic and its applications in three problem areas: (a) qualitative systems analysis; (b) imprecise knowledge representation; and (c) neural network control of fuzzy rule-based systems. These problem areas are of direct relevance to the analysis and design of both control and knowledge-based systems which operate in an environment of uncertainty or imprecision. In the realm of qualitative systems analysis, a basic problem which we have studied is the following. Assume that a system is comprised of an interconnection of n components, with the qualitative input-output relation of each component characterized by a collection of fuzzy it-then rules involving linguistic variables. The problem is to compute the qualitative input-output relation of the system from the specifications of the qualitative input-output relations of its components. An effective solution to this problem has been obtained through the use of what might be called FA-Prolog. FA-Prolog is a subset of Fuzzy Prolog in which the certainty factors are assumed to be equal to one. With this simplifying assumption, the fuzzy if-then rules can be handled in a straight-forward way and the determination of the qualitative input-output relation reduces to the execution of an FA-Prolog program.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 31, 1989
- Accession Number
- ADA221945
Entities
People
- Lotfi A. Zadeh
Organizations
- University of California, Berkeley