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  2013, Vol. 26 Issue (8): 794-799    DOI:
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Incremental Sequential Learning for Fuzzy Neural Networks
HU Rong1, 2, XU Wei-Hong1, 3, GAN Lan1, 4
1.School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094
2.Aviation Institute of Electrical and Electronic Engineering, Changsha Aeronautical Vocational and Technical College, Changsha 410014
3.Computer and Communication Engineering Institute, Changsha University of Science and Technology, Changsha 410015
4.School of Information Engineering, East China Jiaotong University, Nanchang 330013

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Abstract  

To gain a fast, accurate and parsimonious fuzzy neural network, an effective incremental sequential learning algorithm for parsimonious fuzzy neural networks (ISL-FNN)is proposed. The pruning strategy is introduced into the generation of neurons. The error reduction ratio is used to define the influence of input data on the output and the influence is utilized for the generation of neurons. In the parameter learning phase, all the free parameters of hidden units, including the newly created and the originally existing, are updated by the extended Kalman filter method. The performance of ISL-FNN is compared with several existing algorithms on some benchmark problems. Result indicates that ISL-FNN produces similar or even better accuracies with less number of rules.

Key wordsIncremental Learning      Fuzzy Neural Network      Extended Kalman Filter(EKF)     
Received: 20 June 2012     
ZTFLH: TP306.1  
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HU Rong
XU Wei-Hong
GAN Lan
Cite this article:   
HU Rong,XU Wei-Hong,GAN Lan. Incremental Sequential Learning for Fuzzy Neural Networks[J]. , 2013, 26(8): 794-799.
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http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2013/V26/I8/794
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