An Automatic Adjustment Method of Backpropagation Learning Parameters, Using Fuzzy Inference
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概要
- 論文の詳細を見る
In this work, we introduce a fuzzy inference in conventional backpropagation learning algorithm, for networks of neuron like units. This procedure repeatedly adjusts the learning parameters and leads the system to converge at the earliest possible time. This technique is appropriate in a sense that optimum learning parameters are being applied in every learning cycle automatically, whereas the conventional backpropagation doesn't contain any well-defined rule regarding the proper determination of the value of learning parameters.
- 社団法人電子情報通信学会の論文
- 1993-04-25
著者
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Inoue T
Kumamoto University
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Inoue T
Faculty Of Engineering Kumamoto University
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Inoue Takahiro
Dep. Of Computer Sci. And Electrical Engineering Graduate School Of Sci. And Technol. Kumamoto Univ.
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Sugitani Kenichi
the Faculty of Engineering, Kumamoto University
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Ueno Fumio
the Faculty of Engineering, Kumamoto University
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Inoue Takahiro
the Faculty of Engineering, Kumamoto University
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Baloch Badur-ul-haque
the Faculty of Engineering, Kumamoto University
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Yamamoto Takayoshi
Kure Works Babcock-Hitachi K.K.
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Sugitani Kenichi
Faculty Of Engineering Kumamoto University
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Ueno Fumio
The Faculty Of Engineering Kumamoto University
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Inoue T
Graduate School Of Science And Technology Kumamoto University
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Yamamoto T
Okayama Prefectural Univ. Soja‐shi Jpn
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Ueno F
National Coll. Technol. Kumamoto‐ken Jpn
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Baloch Badur-ul-haque
The Faculty Of Engineering Kumamoto University
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Inoue Takahiro
The Faculty Of Engineering Kumamoto University
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