A Method to Combine Chaos and Neural Network Based on the Fixed Point Theory
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- Zhou Diwei
- Tokyo Metropolitan University
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- Yasuda Keiichiro
- Tokyo Metropolitan University
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- Yokoyama Ryuichi
- Tokyo Metropolitan University
書誌事項
- タイトル別名
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- Method to Combine Chaos and Neural Netw
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抄録
The dynamics of either associative or hierarchical neural network can boil down to the discovery of fixed point or contraction to the already established fixed point in a discrete dynamical system, and strict mathematical calculations have already proven this point. In other words, the dynamics of all types of neural network can be analyzed and explained by using the fixed point theory in the traditional discrete dynamical system. What is important is that the fixed point theory also can identify the existence of chaos in the time-space and analyze its characteristics. This has provided an important link between chaos and neural network in the traditional discrete dynamical system, namely, the fixed point theory.<br>Based on this idea, this paper proposes a method to combine chaos with neural network using the fixed point theory. With a view to practical application, the paper provides several examples on improving pattern recognition ability by adding chaotic noise in learning machines as well as on improving the ability of optimal solution in the large by creating a new Chaotic Hopfield Neural Network. The approach proposed by this paper is proved user friendly and universally applicable through lab experiments of pattern recognition and solution of the Traveling Salesman Problem on a set of 100 cities.
収録刊行物
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- 電気学会論文誌C(電子・情報・システム部門誌)
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電気学会論文誌C(電子・情報・システム部門誌) 117 (5), 599-608, 1997
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679584912384
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- NII論文ID
- 130006843981
- 10002810233
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 4204790
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
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