Art

J-GLOBAL ID：200902146869306548   Reference number：01A0048932

On a Weight Limit Approach for Enhancing Fault Tolerance of Feedforward Neural Networks.

フィードフォワード神経回路網のフォールトトレランスを高めるための重み付け制限手法

Author (5)： KAMIURA N (Himeji Inst. Technol., Himeji-shi, JPN) ,  ISOKAWA T (Himeji Inst. Technol., Himeji-shi, JPN) ,  HATA Y (Himeji Inst. Technol., Himeji-shi, JPN) ,  MATSUI N (Himeji Inst. Technol., Himeji-shi, JPN) ,  YAMATO K (Hyogo Univ., Kakogawa-shi, JPN)
Material： IEICE Transactions on Information and Systems (Institute of Electronics, Information and Communication Engineers)  (IEICE Trans Inf Syst (Inst Electron Inf Commun Eng))
Volume： E83-D  Issue： 11  Page： 1931-1939  Publication year： Nov. 25, 2000 
JST Material Number： L1371A  ISSN： 0916-8532  Document type： Article
Article type： 原著論文  Country of issue： Japan (JPN)  Language： ENGLISH (EN)

Thesaurus term： *neural network (network) ,...

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JST classification (2)： Artificial intelligence  (JE08000Z) ,  Pattern recognition  (JE07000S)

Reference (14)：

• HAYKEN, S. Neural Networks, A Comprehensive Foundation. 1994
• GRIFFIN, M. An 11-million transistor neural network execution engine. IEEE Int. Solid-State Circuits Conf. Dig. Tech. Papers. 1991, 180-181
• ISHII, H. Hardware-backpropagation learning of neuron MOS neural networks. Int. Electron Devices Meeting Tec. Dig. 1992, 435-438
• KOSAKA, H. An excellent weight-updating-linerarity EEPROM synapse memory cell for self-learning neuron-MOS neural networks. IEEE Trans. Electron Devices. 1995, 42, 1, 135-143
• WHITE, B. A. The digi-neocognitron: A digital neocognitron neural network model for VLSI. IEEE Trans. Neural Networks. 1992, 3, 1, 73-85

Terms in the title (6)： フィードフォワード ,  神経回路網 ,  フォールトトレランス ,  重み付け ,  制限 ,  手法