Fuzzy associative matrix

About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.

Genetic optimization of fuzzy fractional PD+I controllers

Abstract: Fractional order calculus is a powerful emerging mathematical tool in science and engineering. There is currently an increasing interest in generalizing classical control theories and developing novel control strategies. The genetic algorithms (GA) are a stochastic search and optimization methods based on the reproduction processes found in biological systems, used for solving engineering problems. In the context of process control, the fuzzy logic usually means variables that are described by imprecise terms, and represented by quantities that are qualitative and vague. In this article we consider the development of an optimal fuzzy fractional PD+I controller in which the parameters are tuned by a GA. The performance of the proposed fuzzy fractional control is illustrated through some application examples.

Constructing fuzzy cognitive maps

Abstract: This paper describes the method for automatically constructing fuzzy cognitive maps based on the user-provided data. This method consists of finding the degree of similarity between any two variables (represented by numerical vectors), finding whether the relation between variables is direct or inverse, and with the help of the fuzzy expert system tool (FEST) it establishes the causality among variables. >

Automatic fuzzy model identification for short-term load forecast

Abstract: The conventional fuzzy modelling of short-term load forecasting has a drawback in that the fuzzy rules or the fuzzy membership functions are determined by trial and error. An automatic model identification procedure is proposed to construct the fuzzy model for short-term load forecast. An analysis of variance is used to identify the influential variables of the system load. To set up the fuzzy rules, a cluster estimation method is adopted to determine the number of rules and the membership functions of variables involved in the premises of the rules. A recursive least squares method is then used to determine the coefficients in the concluding parts of the rules. None of these steps involves nonlinear optimisation and all steps have well bounded computation time. This method was tested on the Taiwan Power Company's (Taipower) load data and the performance of the proposed method is compared to those of Box-Jenkins (B-J) transfer function and artificial neural network (ANN) models.

A Method for Ranking of Fuzzy Numbers Using New Weighted Distance

Abstract: In this paper, the researchers proposed a modified new weighted distance method to rank fuzzy numbers. The modified method can effectively rank various fuzzy numbers, their images and overcome the shortcomings of the previous techniques. The proposed model is studied for a broad class for fuzzy numbers and class of functions the membership of which is formed on the basis of the template ( ) max (0.1 ) s  x   x . This article also used some comparative examples to illustrate the advantage of the proposed method.