NDLI: Systems identification using type-2 fuzzy neural network (type-2 FNN) systems

Content Provider	IEEE Xplore Digital Library
Author	Ching-Hung Lee Yu-Ching Lin Wei-Yu Lai
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. Eng., Yuan-Ze Univ., Chung-li, Taiwan (Ching-Hung Lee; Yu-Ching Lin; Wei-Yu Lai)
Abstract	This paper presents a type-2 fuzzy neural network system (type-2 FNN) and its learning algorithm using back-propagation algorithm. In our previous results, the FNN system using type-1 fuzzy logic systems (FLS) is called type-1 FNN system. It has the properties of parallel computation scheme, easy to implement, fuzzy logic inference system, and parameters convergence. For considering the fuzzy rules uncertainties, we use the type-2 FLSs to develop a type-2 FNN system. The type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-based fuzzy logic systems (FLSs). In this paper, the previous results of type-1 FNN are extended to a type-2 one. In addition, the corresponding learning algorithm is derived by back-program algorithm. Several examples are presented to illustrate the effectiveness of our approach.
Sponsorship	IEEE Robotics & Automation Soc
Starting Page	1264
Ending Page	1269
File Size	336130
Page Count	6
File Format	PDF
ISBN	0780378660
DOI	10.1109/CIRA.2003.1222178
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-16
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks System identification Fuzzy logic Fuzzy sets Signal processing algorithms Neural networks Fuzzy control Uncertainty Fuzzy systems Convergence
Content Type	Text
Resource Type	Article

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