A Fast Heuristic Global Learning Algorithm for Multilayer Neural Networks
作者： Siu-yeung Cho , Tommy W.S. Chow
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摘要: This paper presents a novel Heuristic Global Learning (HER-GBL) algorithm for multilayer neural networks. The is based upon the least squares method to maintain fast convergence speed, and penalized optimization solve problem of local minima. penalty term, defined as Gaussian-type function weight, provide an uphill force escape from As result, training performance dramatically improved. proposed HER-GBL yields excellent results in terms avoidance minima quality solution.
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