Hard partition-based non-fuzzy inference system for nonlinear process

Authors:
Keon-Jun Park;Jun-Myung Lee;Yong-Kab Kim
Affiliations:
Department of Information and Communication Engineering, Wonkwang University, Iksan-si, Chonbuk, South Korea;Department of Information and Communication Engineering, Wonkwang University, Iksan-si, Chonbuk, South Korea;Department of Information and Communication Engineering, Wonkwang University, Iksan-si, Chonbuk, South Korea
Venue:
FGIT'12 Proceedings of the 4th international conference on Future Generation Information Technology
Year:
2012

Citing 2
Cited 0

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Numerical and applicational aspects of fuzzy relational equations

Fuzzy Sets and Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We introduec a non-fuzzy inference system based on hard partition to contruct model for nonlinear process. In fuzzy modeling, the generation of fuzzy rules has the problem that the number of fuzzy rules exponentially increases. To solve this problem, the rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions. The proposed model is evaluated with the performance using the data widely used in nonlinear process.