A Modified Gradient-Based Neuro-Fuzzy Learning Algorithm for Pi-Sigma Network Based on First-Order Takagi-Sugeno System
Received:July 19, 2012  Revised:November 25, 2012
Key Word: first-order Takagi-Sugeno inference system   Pi-Sigma network   convergence.  
Fund ProjectL:Supported by the Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China (Grant No.11171367) and the Youth Foundation of Dalian Polytechnic University (Grant No.QNJJ 201308).
Author NameAffiliation
Yan LIU School of Mathematical Sciences, Dalian University of Technology, Liaoning 116024, P. R. China
School of Information Science and Engineering, Dalian Polytechnic University, Liaoning 116034, P. R. China 
Jie YANG School of Mathematical Sciences, Dalian University of Technology, Liaoning 116024, P. R. China 
Dakun YANG School of Mathematical Sciences, Dalian University of Technology, Liaoning 116024, P. R. China 
Wei WU School of Mathematical Sciences, Dalian University of Technology, Liaoning 116024, P. R. China 
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Abstract:
      This paper presents a Pi-Sigma network to identify first-order Tagaki-Sugeno (T-S) fuzzy inference system and proposes a simplified gradient-based neuro-fuzzy learning algorithm. A comprehensive study on the weak and strong convergence for the learning method is made, which indicates that the sequence of error function goes to a fixed value, and the gradient of the error function goes to zero, respectively.
Citation:
DOI:10.3770/j.issn:2095-2651.2014.01.012
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