Evolutionary Interval Type-2 Fuzzy Systems Using Continuous Ant Colony Optimization Algorithms

被引：0

作者：

Juang, Chia-Feng ^{[1
]}

Hung, Chi-Wei ^{[1
]}

机构：

[1] Natl Chung Hsing Univ, Dept Elect Engn, Taichung, Taiwan

来源：

PROCEEDINGS OF THE 2016 IEEE 11TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA) | 2016年

关键词：

evolutionary fuzzy systems; ant colony optimization; type-2 fuzzy systems; swarm intelligence; PARTICLE-SWARM OPTIMIZATION; NEURAL-NETWORK; CONTROLLER; DESIGN;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes the optimization of interval type-2 fuzzy systems (IT2FSs) through continuous ant colony optimization (ACO) algorithm. The optimized IT2FS consists of Mamdani-type fuzzy rules, where the antecedent and consequent parts use interval type-2 Gaussian fuzzy sets with uncertain means. Given the structure of an IT2FS, this paper proposes the optimization of all the free parameters in it through two types of continuous ACO algorithms. The first one is ant colony optimization in real space, where a colony of solution vectors is created with each vector comprising all of the free parameters in an IT2FS. The second one is cooperative continuous ACO (CCACO), where multiple colonies are created with each solution vector in a colony comprising only the free parameters in a single rule. Simulations are presented to the show the performance of the continuous ACO algorithms for IT2FS design with comparisons with different type-1 and type-2 fuzzy systems.

引用

页码：26 / 31

页数：6

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