---
abstract: |-
  The chaotic neural network constructed with chaotic neurons exhibits very rich dynamic
  behaviors and has a nonperiodic associative memory. In the chaotic neural network,
  however, it is dicult to distinguish the stored patters from others, because the states of
  output of the network are in chaos. In order to apply the nonperiodic associative memory
  into information search and pattern identication, etc, it is necessary to control chaos in
  this chaotic neural network. In this paper, the phase space constraint method focused on
  the chaotic neural network is proposed. By analyzing the orbital of the network in phase
  space, we chose a part of states to be disturbed. In this way, the evolutional spaces of
  the strange attractors are constrained. The computer simulation proves that the chaos
  in the chaotic neural network can be controlled with above method and the network can
  converge in one of its stored patterns or their reverses which has the smallest Hamming
  distance with the initial state of the network. The work claries the application prospect
  of the associative dynamics of the chaotic neural network.
altloc: []
chapter: ~
commentary: ~
commref: ~
confdates: ~
conference: ~
confloc: ~
contact_email: ~
creators_id: []
creators_name:
  - family: HE
    given: Guo-guang
    honourific: Dr
    lineage: ''
  - family: CAO
    given: Zhi-tong
    honourific: Prof.
    lineage: ''
  - family: CHEN
    given: Hong-ping
    honourific: Dr.
    lineage: ''
  - family: ZHU
    given: Ping
    honourific: Dr
    lineage: ''
date: 2003-09
date_type: published
datestamp: 2005-09-18
department: ~
dir: disk0/00/00/45/38
edit_lock_since: ~
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editors_id: []
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eprint_status: archive
eprintid: 4538
fileinfo: /style/images/fileicons/application_pdf.png;/4538/1/IJMPB%2D17%2D4209.pdf
full_text_status: public
importid: ~
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isbn: ~
ispublished: pub
issn: ~
item_issues_comment: []
item_issues_count: 0
item_issues_description: []
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keywords: ~
lastmod: 2011-03-11 08:56:11
latitude: ~
longitude: ~
metadata_visibility: show
note: ~
number: ' 22-24'
pagerange: 4209-4214
pubdom: FALSE
publication: ' International Journal of Modern Physics B'
publisher: ~
refereed: FALSE
referencetext: |-
  1. CAO Zhitong,\The dynamic associative memory of chaotic neural", J. of Zhejiang
  University (Natural Science Edition), Supplement: p. 330{335 (in Chinese), 1998.
  2. K. Aihara, T. Takabe and M. Toyoda, \chaotic neural networks". Phys Lett 144(6{7),
  333{340 (1990).
  3. M. Adachi and K. Aihara \Associative dynamics in a chaotic neural network", Neural
  Networks 10(1), 83{98 (1997).
  4. E. R. Kobori, K. Ikoda and K. Nakayama, \A model of dynamic associative memory",
  IEEE International Conference on Neural Networks Conference Proceedings 2, 804{809
  (1996).
  5. He Guoguang and Cao Zhitong, \Controlling chaos in chaotic neural network", Acta
  Physica Sinica 50(11), p. 2103{2107 (in Chinese), 2001.
  6. E. Ott, C. Grebogi and J. A. Yorke, \Controlling chaos", Phys. Rev. Lett. 64(11),
  p. 1196{1199 (1990).
  7. Pyragas, K. \Continuous Control of Chaos by Self-Controlling Feedback", Phys. Lett.
  A170, p. 421{428 (1992).
  8. X. S. Luo, \Using phase space compression to control chaos and hyperchaos", Acta
  Physica Sinica, 48(3), p. 402{406 (in Chinese), 1999.
relation_type: []
relation_uri: []
reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 17:00:40
subjects:
  - comp-sci-mach-dynam-sys
succeeds: ~
suggestions: ~
sword_depositor: ~
sword_slug: ~
thesistype: ~
title: Controlling Chaos in a Neural Network Based on the Phase Space Constraint
type: journalp
userid: 3813
volume: 17
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