Annealed cooperative-competitive learning of Mahalanobis-NRBF neural modules for nonlinear and chaotic differential function approximation

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• 作者：Jiann-Ming Wu ; ^{jmwu@livemail.tw" class="auth_mail} ; ^{jmwu@mail.ndhu.edu.tw" class="auth_mail}Author Vitae ; Chun-Chang WuAuthor Vitae ; Ching-Wen HuangAuthor Vitae
• 关键词：Multilayer neural networks ; Free energy function ; Mixed integer programming ; Mean field annealing ; Long term prediction ; Chaotic time series
• 刊名：Neurocomputing
• 出版年：20 July, 2014
• 年：2014
• 卷：136
• 期：Complete
• 页码：56-70
• 全文大小：4707 K

文摘

This work explores annealed cooperative-competitive learning of multiple modules of Mahalanobis normalized radial basis functions (NRBF) with applications to nonlinear function approximation and chaotic differential function approximation. A multilayer neural network is extended to be composed of multiple Mahalanobis-NRBF modules. Each module activates normalized outputs of radial basis functions, determining Mahalanobis radial distances based on its own adaptable weight matrix. An essential cooperative scheme well decomposes learning a multi-module network to sub-tasks of learning individual modules. Adaptable network interconnections are asynchronously updated module-by-module based on annealed cooperative-competitive learning for function approximation under a physical-like mean-field annealing process. Numerical simulations show outstanding performance of annealed cooperative-competitive learning of a multi-module Mahalanobis-NRBF network for nonlinear function approximation and long term look-ahead prediction of chaotic time series.