Optimal solutions for cellular neural networks by paralleled hardware annealing

Sa H. Bang*, Bing J. Sheu, Tony H.Y. Wu

*此作品的通信作者

研究成果: 期刊稿件文章同行評審

10 引文 斯高帕斯(Scopus)

摘要

An engineering annealing method for optimal solutions of cellular neural networks is presented. Cellular neural networks are very promising in solving many scientific problems in image processing, pattern recognition, and optimization by the use of stored program with predetermined templates. Hardware annealing, which is a paralleled version of mean-field annealing in analog networks, is a highly efficient method of finding optimal solutions of cellular neural networks. It does not require any stochastic procedure and henceforth can be very fast The generalized energy function of the network is first increased by reducing the voltage gain of each neuron. Then, the hardware annealing searches for the globally minimum energy state by continuously increasing the gain of neurons. The process of global optimization by the proposed annealing can be described by the eigenvalue problems in the time-varying dynamic system. In typical nonoptimization problems, it also provides enough stimulation to frozen neurons caused by ill-conditioned initial states.

原文英語
頁(從 - 到)440-454
頁數15
期刊IEEE Transactions on Neural Networks
7
發行號2
DOIs
出版狀態已出版 - 1996
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