Showing papers on "Fuzzy associative matrix published in 1987"

Multi‐objective optimization of fuzzy structural systems

Abstract: It is recognized that there exists a vast amount of fuzzy information in both the objective and constraint functions of the optimum design of structures. Since most practical structural design problems involve several, often conflicting, objectives to be considered, a multi-objective fuzzy programming method is outlined in this work. The fuzzy constraints define a fuzzy feasible domain in the design space and each of the fuzzy objective functions defines the optimum solution by a fuzzy set of points. A method of solving a fuzzy multi-objective structural optimization problem using ordinary single-objective programming techniques is presented. The computational approach is illustrated with two numerical examples.

A parallel rule firing fuzzy production system with resolution of memory conflicts by weak fuzzy monoticity, applied to the classification of multiple objects characterized by multiple uncertain features

Abstract: A fuzzy production system shell is described characterized by parallel rather than sequential rule firing. All fireable rules are fired in effect concurrently. Since there is no unfired-rule stack, no backtracking can take place, and no rule conflict algorithm is necessary; instead, a memory conflict algorithm is invoked when more than one rule modifies the same datum. Memory conflicts are resolved by weakly monotonie fuzzy logic; i.e. the value or truth value of an attribute may be replaced if the new truth value is equal to or greater than the old truth value. The system depends heavily on the use of fuzzy logic and on confidence levels, fuzzy numbers and fuzzy sets as explicit data types, and on the generation of rules from a data base of expert knowledge. Fuzzy sets are used to store contradictory and ambiguous information and results. If a problem is suitable for parallel processing, substantial reductions in system overhead are achieved, together with substantial economy in the number of rules which must be written; if a problem is not suitable for parallel processing, no economy is achieved. We suggest that problems which yield to deductive reasoning constitute a class which is suitable for sequential rule firing, and problems which yield to inductive reasoning constitute a class suitable for parallel processing. A successful application of the system to the unsupervised analysis of a time sequence of noisy echocardiogram images is described.

A Fuzzy Decision Making Method for Multi-Objective Problems with a Preference Structure Representation by Fuzzy Connectives

Abstract: The authors propose a new fuzzy decision making method for multi-objective problems. The main feature of this method is the usage of fuzzy connectives to represent the decisionmaker's preference structure. The fuzzy connectives which we adopt are Zimmermann's ƒÁ operation and its extensions. These extended operations have much larger descriptivity of the decision-maker's preference structure than the ƒÁ operation. Our method is summarized in the following 5 steps. Step 1. The identification of a decision making problem with a fuzzy environment and the construction of an objective hierarchy. Step. 2. A questionnaire procedure: Questionnaire is used to identify the decision-maker's

Operations on Fuzzy sets

Abstract: Fuzzy sets are introduced as a generalization of ordinary sets. In this chapter we are going to define an algebraic structure of the class of fuzzy sets which will be useful in the combination of vague data. If an expert is sure, for example, that the ”true” value of the price of a house is in [200,000; 300,000] and another expert has information that the price of the same house is in [250,000; 350,000], we can calculate the intersection of these sets in order to aggregate the two expert opinions. In other situations different operations may be useful; thus, we start to discuss the basic operation performed on fuzzy sets which were originally proposed by L.A. Zadeh [212].