A new method for weighted fuzzy interpolative reasoning based on PSO-based weights-learning techniques

Author

Chen, Shyi-ming ; Hsin, Wen-chyuan ; Chang, Yu-chuan

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan

Volume

4

fYear

2012

fDate

15-17 July 2012

Firstpage

1454

Lastpage

1460

Abstract

In this paper, we present a weighted fuzzy interpolative reasoning method based on the proposed PSO-based weights-learning algorithm. We also apply the proposed method to deal with the computer activity prediction problem. The experimental results show that the proposed weighted fuzzy interpolative reasoning method using the optimally learned weights obtained by the proposed PSO-based weights-learning algorithm gets smaller relative squared error rates than the existing methods.

Keywords

fuzzy reasoning; fuzzy set theory; interpolation; learning (artificial intelligence); particle swarm optimisation; PSO-based weights-learning techniques; computer activity prediction problem; particle swarm optimisation; relative squared error rates; weighted fuzzy interpolative reasoning method; Abstracts; TV; Fuzzy Rules; Fuzzy sets; PSO-Based Weights-Learning; Weighted Fuzzy Interpolative Reasoning;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2012 International Conference on

Conference_Location

Xian

ISSN

2160-133X

Print_ISBN

978-1-4673-1484-8

Type

conf

DOI

10.1109/ICMLC.2012.6359579

Filename

6359579