Optimal solutions for cellular neural networks by paralleled hardware annealing

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

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

10 Scopus citations


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.

Original languageEnglish
Pages (from-to)440-454
Number of pages15
JournalIEEE Transactions on Neural Networks
Issue number2
StatePublished - 1996
Externally publishedYes


Dive into the research topics of 'Optimal solutions for cellular neural networks by paralleled hardware annealing'. Together they form a unique fingerprint.

Cite this