一种连续增量学习模糊神经网络

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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 words： Incremental Learning Fuzzy Neural Network Extended Kalman Filter(EKF)

Received: 20 June 2012

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	Articles by authors
	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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