| 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 Words:
first-order Takagi-Sugeno inference system Pi-Sigma network convergence.
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| Fund Project: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 Name | Affiliation | | 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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