--- abstract: 'The chaotic neural network constructed with chaotic neuron shows the associative memory function, but its memory searching process cannot be stabilized in a stored state because of the chaotic motion of the network. In this paper, a pinning control method focused on the chaotic neural network is proposed. The computer simulation proves that the chaos in the chaotic neural network can be controlled with this method and the states of the network can converge in one of its stored patterns if the control strength and the pinning density are chosen suitable. It is found that in general the threshold of the control strength of a controlled network is smaller at higher pinned density and the chaos of the chaotic neural network can be controlled more easily if the pinning control is added to the variant neurons between the initial pattern and the target pattern.' altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: - gghe - '' - '' - '' creators_name: - family: He given: G. honourific: Dr lineage: '' - family: Cao given: Z. honourific: Prof. lineage: '' - family: Zhu given: P. honourific: Prof. lineage: '' - family: Ogura given: H. honourific: Prof. lineage: '' date: 2003 date_type: published datestamp: 2004-04-06 department: ~ dir: disk0/00/00/30/01 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 3001 fileinfo: /style/images/fileicons/application_pdf.png;/3001/1/NN03.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: inpress issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: Chaotic dynamic; Chaotic neural network; Controlling chaos; Pinning control method lastmod: 2011-03-11 08:55:17 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: ~ pubdom: FALSE publication: Neural Networks publisher: Elsevier Science Ltd. refereed: FALSE referencetext: | 1． Aihara, K., Takabe, T., & Toyoda, M. (1990). Chaotic Neural Networks. Phys. Lett. A, 144, 333-340. 2． Adachi, M., & Aihara, K. (1997). Associative Dynamics in a Chaotic Neural Network.. Neural Networks, 10, 83-98. 3． Tokuda, I., Nagashima, T. & Aihara, K. (1997). Global Bifurcation Structure of Chaotic Neural Networks and Its Application to Traveling Salesman Problem. Neural Networks, 10, 1673-1690. 4． Freeman, W. J. (1987). Simulation of chaotic EEG patterns with a dynamic model of the olfactory system. Biological Cybernetics, 56, 139-150. 5． Degn, H., Holden, A. V. & Olsen L. F. (Eds.),(1987). Chaos in biological systems. New York: Plenum. 6． Tsuda, I. (1991). Chaotic itinerancy as a dynamical basis of hermeneutics in brain and mind. World Futures, 32, 167-184. 7． Kobori, E. R., Ikoda, K. & Nakayama, K. (1996). Model of dynamic associative memory. IEEE International Conference on Neural Networks Conference Proceedings, 2, 804-809. 8． Ott, E., Grebogi, C. & Yorke, J. A. (1990). Controlling Chaos. Phys. Rev. Lett., 64, 1196-1199. 9． Pecora, L. M. & Carroll, T. L. (1990). Synchronization in Chaotic Systems. Phys. Rev. Lett., 64, 821-824. 10． Hunt, E R. (1991). Stabling High-Period Orbits in a Chaotic System: the Diode Resonator. Phys. Rev. Lett., 67, 1953-1955. 11． Pyragas, K. (1992). Continuous Control of Chaos by Self-Controlling Feedback. Phys. Lett. A, 170, 421-428. 12． Hu G., & Qu Z. (1994). Controlling Spatiotemporal Chaos in Coupled Map Lattice Systems. Phys. Rev. Lett., 72, 68-77. 13． Adachi, M. (1995). Controlling a Simple Chaotic Neural Network using response to perturbation. Proc. NOLTA’95, 989-992. 14． Mizutani, S., Sato, T., Uchiyama, T. & Sonehara, N. (1995). Controlling Chaos in Neural Networks, Proc. ICNN, Perth 6, 3038-3043. 15． Shimada, I., & Nagashima, T. (1979). A numerical approach to ergodic problem of dissipative dynamical systems. Progress of Theoretical Physics, 61, 1605-1616 relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 16:47:48 subjects: - comp-sci-mach-dynam-sys - comp-sci-neural-nets - comp-sci-art-intel succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: Controlling chaos in a chaotic neural network type: journalp userid: 3813 volume: ~