利用连续Hopfield神经网络求解聚类问题的模型构造

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Modeling Optimal Clustering Based on Continuous Hopfield Neural Network

WANG Mao-zhi, GUO Ke, XU Wen-xi, FAN An-dong

School of Management of Information, Chengdu University of Technology, Chengdu 610059, China

Received:2004-09-15 Revised:2004-12-03 Online:2006-01-31 Published:2006-01-31

Abstract

Abstract: Clustering is a combinational optimal calculation based on its mathematical description. A model combined with continuous Hopfield neural network and used to solve optimal clustering is designed and constructed based on Hopfield’s optimal ability. Details of network mapping, energy function construction and nerve state changing equation are described. Their simplified formation is presented according to the winner-tale-all competitive mechanism. Convergence of the model is proved. Effectiveness and rationality of the model is verified in an application to image compression coding.

Key words:

cluster, combination optimal, neural network, energy function, convergence

WANG Mao-zhi;GUO Ke;XU Wen-xi;FAN An-dong. Modeling Optimal Clustering Based on Continuous Hopfield Neural Network
[J]. Journal of Applied Sciences.

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Modeling Optimal Clustering Based on Continuous Hopfield Neural Network

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