Avoiding the Local Minima Problem in Backpropagation Algorithm with Modified Error Function(Neural Networks and Bioengineering)

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概要

• 論文の詳細を見る

• One critical "drawback" of the backpropagation algorithm is the local minima problem. We have noted that the local minima problem in the backpropagation algorithm is usually caused by update disharmony between weights connected to the hidden layer and the output layer. To solve this kind of local minima problem, we propose a modified error function with two terms. By adding one term to the conventional error function, the modified error function can harmonize the update of weights connected to the hidden layer and those connected to the output layer. Thus, it can avoid the local minima problem caused by such disharmony. Simulations on some benchmark problems and a real classification task have been performed to test the validity of the modified error function.

• 社団法人電子情報通信学会の論文
• 2005-12-01

著者

• TANG Zheng

  Faculty of Engineering, Toyama University

• TAMURA Hiroki

  Faculty of Engineering, Toyama University

• Tang Zheng

  Univ. Toyama Toyama‐shi Jpn

• Tamura Hiroki

  Faculty Of Engineering Toyama University

• Tang Zheng

  Faculty Of Engineering Miyazaki University

• TANG Zheng

  Graduate School of Innovative Life Science, University of Toyama

• BI Weixing

  Faculty of Engineering, Toyama University

• WANG Xugang

  Intelligence Engineering Laboratory, Institute of Software, The Chinese Academy of Sciences

• Tang Zheng

  Univ. Of Toyama Toyama‐shi Jpn

• Tang Zheng

  Graduate School Of Innovative Life Science University Of Toyama

• Bi Weixing

  Faculty Of Engineering Toyama University

• Wang Xugang

  Intelligence Engineering Laboratory Institute Of Software The Chinese Academy Of Sciences

• TAMURA Hiroki

  University of Miyazaki

• Tang Zheng

  Department Of Computer Science And Technology The Key Laboratory Of Embedded System And Service Comp

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