An improved learning algorithm for rule refinement in neuro-fuzzy modeling

Author

Ouyang, Chen-Sen ; Lee, Shie-Jue

Author_Institution

Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan

fYear

1999

fDate

36495

Firstpage

238

Lastpage

241

Abstract

We propose an improved learning algorithm for rule refinement in neuro-fuzzy modeling. This algorithm is mainly based on a well-known technique, i.e., singular value decomposition (SVD). By using the method of SVD, the learning algorithm can converge quickly. Besides, the reasoning operator adopted in our algorithm is a compensatory fuzzy operator which has the advantage of being more adaptive and effective. Experimental results show that the proposed algorithm converges quickly and the obtained fuzzy rules are more precise

Keywords

fuzzy neural nets; fuzzy set theory; knowledge acquisition; learning (artificial intelligence); modelling; singular value decomposition; SVD; compensatory fuzzy operator; fuzzy rules; improved learning algorithm; neuro-fuzzy modeling; reasoning operator; rule refinement; singular value decomposition; Backpropagation algorithms; Convergence; Data mining; Fuzzy control; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Neural networks; Singular value decomposition;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge-Based Intelligent Information Engineering Systems, 1999. Third International Conference

Conference_Location

Adelaide, SA

Print_ISBN

0-7803-5578-4

Type

conf

DOI

10.1109/KES.1999.820163

Filename

820163