DocumentCode :
578420
Title :
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
Link To Document :
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