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

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3 Author(s)
Ching-Hung Lee ; Dept. of Electr. Eng., Yuan-Ze Univ., Chung-li, Taiwan ; Yu-Ching Lin ; Wei-Yu Lai

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.

Published in:

Computational Intelligence in Robotics and Automation, 2003. Proceedings. 2003 IEEE International Symposium on  (Volume:3 )

Date of Conference:

16-20 July 2003

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