Presentation 1999/3/18
Novel Patterns Learning by Utilizing the Bifurcation of the Internal State of Network
Kazuhiro KOJIMA, Koji ITO,
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Abstract(in English) In conventional associative model such as Hopfield model, the information processes have to be divided into the two process, i.e., associative phase changed by the external observers. But there are not any external observers in brain and neural systems. We propose the autonomous novel pattern learning method by utilizing the bifurcation of the internal state of the network. We constructed the neural network from Lorenz systems to realize "I don't know" state. Further, in computer simulatoions, the dynamical properties of the network are studied. It is shown that when no embedded pattern, i.e , unknown pattern, is given to the network, the network state oscillates chaotically. It is considered as "I don't know" state. Further, when the proposed learning method is applied to the network, the network can change the associative phase and learning phase autonomously. Furthermore, the network can learn new patterns without destroying the previous embedded patterns.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Chaotic Dynamics / Mutually Combined Lorenz System / I don't know state / Bifurcation / Autonomous Learning
Paper # NC98-139
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Conference Information
Committee NC
Conference Date 1999/3/18(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Novel Patterns Learning by Utilizing the Bifurcation of the Internal State of Network
Sub Title (in English)
Keyword(1) Chaotic Dynamics
Keyword(2) Mutually Combined Lorenz System
Keyword(3) I don't know state
Keyword(4) Bifurcation
Keyword(5) Autonomous Learning
1st Author's Name Kazuhiro KOJIMA
1st Author's Affiliation Interdisciplinary Graduate School of Science and Engineering, Department of Computational Intelligence and Systems Science, Tokyo Institude of Technology()
2nd Author's Name Koji ITO
2nd Author's Affiliation Interdisciplinary Graduate School of Science and Engineering, Department of Computational Intelligence and Systems Science, Tokyo Institude of Technology
Date 1999/3/18
Paper # NC98-139
Volume (vol) vol.98
Number (no) 673
Page pp.pp.-
#Pages 8
Date of Issue