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A reference model approach to stability analysis of neural networks
Qiao, H; Peng, J; Xu, ZB; Zhang, B
2003-12
发表期刊IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
卷号33期号:6页码:925-936
摘要
; In this paper, a novel methodology called a reference model approach to stability analysis of neural networks is proposed. The core of the new approach is to study a neural network model with reference to other related models, so that different modeling approaches can be combinatively used and powerfully cross-fertilized. Focused on two representative neural network modeling approaches (the neuron state modeling approach and the local field modeling approach), we establish a rigorous theoretical basis on the feasibility and efficiency of the reference model approach. The new approach has been used to develop a series of new, generic stability theories for various neural network models. These results have been applied to several typical neural network systems including the Hopfield-type neural networks, the recurrent back-propagation neural networks, the BSB-type neural networks, the bound-constraints optimization neural networks, and the cellular neural networks. The results obtained unify, sharpen or generalize most of the existing stability assertions, and illustrate the feasibility and power of the new method.
关键词Local Field Neural Network Model Reference Model Approach Stability Analysis Static Neural Network Model
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12612
专题09年以前成果
通讯作者Qiao, H
作者单位Univ Manchester, Inst Sci & Technol, Dept Computat
推荐引用方式
GB/T 7714
Qiao, H,Peng, J,Xu, ZB,et al. A reference model approach to stability analysis of neural networks[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,2003,33(6):925-936.
APA Qiao, H,Peng, J,Xu, ZB,&Zhang, B.(2003).A reference model approach to stability analysis of neural networks.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,33(6),925-936.
MLA Qiao, H,et al."A reference model approach to stability analysis of neural networks".IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 33.6(2003):925-936.
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