A non-neural fuzzy rule base approach for modeling complex systems

Author

Applebaum, Ellen

Author_Institution

Dept. of Math., Colorado Univ., Denver, CO, USA

fYear

1997

fDate

21-24 Sep 1997

Firstpage

130

Lastpage

135

Abstract

Studies the modeling of complex systems using fuzzy rules for the description of the underlying plant. The current research is a modification and an extension of a bisection and homogeneity algorithm that generates rules directly from the data. This modified approach examines correlation-product inference as a method for approximating nonlinear 2D systems and 3D chaotic systems. The iterative process of trajectory reconstruction combines fuzzy inference and a variable-time-step Euler method. Experimental results are presented for the Van der Pol oscillator and for the Lorenz system

Keywords

chaos; control system analysis computing; correlation theory; digital simulation; fuzzy logic; inference mechanisms; knowledge based systems; large-scale systems; modelling; nonlinear systems; oscillators; uncertainty handling; 3D chaotic systems approximation; Lorenz system; Van der Pol oscillator; bisection algorithm; complex systems modelling; correlation-product inference; fuzzy inference; homogeneity algorithm; iterative process; nonlinear 2D systems approximation; nonneural fuzzy rule base; rule generation; trajectory reconstruction; underlying plant description; variable-time-step Euler method; Automatic control; Chaos; Cranes; Fuzzy systems; Neural networks; Piecewise linear techniques; Polynomials; Round robin; State-space methods; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 1997. NAFIPS '97., 1997 Annual Meeting of the North American

Conference_Location

Syracuse, NY

Print_ISBN

0-7803-4078-7

Type

conf

DOI

10.1109/NAFIPS.1997.624024

Filename

624024