Adaptive Simulated Annealing in CNN Template Learning
Brett Chandler, Csaba Rekeczky, Yoshifumi Nishio, Akio Ushida · 1999
Introduction Opportunities for the application of template optimization (or "learning") for a Cellular Neural Network (CNN) [1] are prevalent insG hareas as pattern recognition andtexture clasturedG50G Itis highly des788Ed to employ an algorithm which not only can produce optimal simald0GT but which can als findthesdthed5 e#ciently, in as sGGG a timeas posM507d Various template-learning methods have been propos6E5 date [2]. In [3] and[4], a hybridDirect-Search methodandSimulatedAnnealing (SA) were inves8F gatedforDisford00G6d CNN template optimization. Another algorithm, whichhas been widelyuse for CNN template learningtasni is the Genetic Algorithm (GA) [5], [6]. A variant of GA, from aclas calledEvolutionaryStrategies was appliedin [7] to obtain featureextraction templates Inthis letter, we compare the performance of a recently-developed optimization algorithm called Adaptive Simulated Annealing (ASA)agains GA. In a publisG87T8dsds sbl y, ASAsAd5TM5 outperformedGA on asG of