Title :
A non-neural fuzzy rule base approach for modeling complex systems
Author :
Applebaum, Ellen
Author_Institution :
Dept. of Math., Colorado Univ., Denver, CO, USA
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;
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
DOI :
10.1109/NAFIPS.1997.624024
