An improved training algorithm of T-S model HHFNN based on ridge regression function

Author

Yu, Xianchuan ; Dai, Sha ; Hu, Dan

Author_Institution

Coll. of Inf. Sci. & Technol., Beijing Normal Univ., Beijing, China

Volume

1

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

126

Lastpage

130

Abstract

A new training algorithm for hierachical hybrid fuzzy - neural network (HHFNN) based on Takagi - Sugeno (T-S) fuzzy system is proposed in this paper. Triangular membership function is adopted. And to reduce the strong interaction among discrete input variables, coefficient contraction method is employed; ridge regression function is used in the THEN parts of fuzzy rules. At last, pyrimidines medical data is used in simulations; results show that our new algorithm gets an advantage in accuracy over the existing training algorithms for HHFNN and standard BP algorithm.

Keywords

fuzzy neural nets; fuzzy systems; learning (artificial intelligence); regression analysis; BP algorithm; T-S model HHFNN; Takagi-Sugeno fuzzy system; backpropagation; coefficient contraction method; fuzzy rules; hierachical hybrid fuzzy neural network; pyrimidines medical data; ridge regression function; training algorithm; triangular membership function; Artificial neural networks; Fuzzy sets; Fuzzy systems; Input variables; Neurons; Testing; Training; Takagi-Sugeno model; hierarchical hybrid fuzzy - neural network; ridge regression function; training algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5583337

Filename

5583337