A Hammerstein Neuro-Fuzzy Network with an Online Hybrid Construction Algorithm for Dynamic Applications

Author

Wang, Jeen-Shing ; Chen, Yen-Ping

Author_Institution

Nat. Cheng Kung Univ., Tainan

fYear

0

fDate

0-0 0

Firstpage

2104

Lastpage

2111

Abstract

This paper presents a Hammerstein neuro-fuzzy network with an online hybrid construction algorithm for dealing with dynamic applications. The proposed recurrent neuro-fuzzy system possesses two salient features: 1) it is capable of translating the complicated dynamic behavior of a system into a set of simple linguistic "dynamic" rules and into a state-space representation as well, and 2) with an automated hybrid construction algorithm, it can self-construct it network structure with a parsimonious size and satisfactory learning performance. Extensive computer simulations have been conducted to validate the effectiveness of the proposed approach for dynamic applications.

Keywords

fuzzy logic; fuzzy neural nets; fuzzy reasoning; fuzzy systems; recurrent neural nets; Hammerstein neuro-fuzzy network; computer simulation; dynamic application; dynamic system behavior; online hybrid construction algorithm; recurrent neuro-fuzzy system; satisfactory learning performance; state-space representation; Clustering algorithms; Computer simulation; Control systems; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Heuristic algorithms; History; Linear systems; Neurofeedback;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2006 IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9488-7

Type

conf

DOI

10.1109/FUZZY.2006.1681992

Filename

1681992