SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS

Document Type: Research Paper

Authors

1 Faculty of Islamic Azad University, Sirjan branch, ,Sirjan, Ker- man, Iran

2 Department of Computer Engineering, School of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

3 Department of Computer Engineering, School of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

4 Department of Electrical Engineering, School of Engi- neering, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract

In this study, a Multi-Objective Genetic Algorithm (MOGA) is
utilized to extract interpretable and compact fuzzy rule bases for modeling
nonlinear Multi-input Multi-output (MIMO) systems. In the process of non-
linear system identi cation, structure selection, parameter estimation, model
performance and model validation are important objectives. Furthermore, se-
curing low-level and high-level interpretability requirements of fuzzy models
is especially a complicated task in case of modeling nonlinear MIMO systems.
Due to these multiple and conicting objectives, MOGA is applied to yield a set
of candidates as compact, transparent and valid fuzzy models. Also, MOGA
is combined with a powerful search algorithm namely Di
erential Evolution
(DE). In the proposed algorithm, MOGA performs the task of membership
function tuning as well as rule base identi cation simultaneously while DE
is utilized only for linear parameter identi cation. Practical applicability of
the proposed algorithm is examined by two nonlinear system modeling prob-
lems used in the literature. The results obtained show the e
ectiveness of the
proposed method.

Keywords


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