Global exponential stability of recurrent neural networks with time-dependent switching dynamics

Zhigang Zeng; Jun Wang; Tingwen Huang

doi:10.1007/978-3-642-04277-5_59

Global exponential stability of recurrent neural networks with time-dependent switching dynamics

Zhigang Zeng, Jun Wang, Tingwen Huang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

In this paper, the switching dynamics of recurrent neural networks are studied. Sufficient conditions on global exponential stability with an arbitrary switching law or a dwell time switching law and the estimates of Lyapunov exponent are obtained. The obtained results can be used to analyze and synthesize a family of continuous-time configurations with the switching between the configurations. Specially, the obtained results are new and efficacious for the switching between the stable and unstable configurations. Finally, simulation results are discussed to illustrate the theoretical results. © 2009 Springer Berlin Heidelberg.

Original language	English
Title of host publication	Artificial Neural Networks - ICANN 2009
Subtitle of host publication	19th International Conference, Proceedings
Publisher	Springer Verlag
Pages	583-592
Volume	5769 LNCS
ISBN (Print)	3642042767, 9783642042768
DOIs	https://doi.org/10.1007/978-3-642-04277-5_59
Publication status	Published - 2009
Externally published	Yes
Event	19th International Conference on Artificial Neural Networks, ICANN 2009 - Limassol, Cyprus Duration: 14 Sept 2009 → 17 Sept 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5769 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Artificial Neural Networks, ICANN 2009
Country/Territory	Cyprus
City	Limassol
Period	14/09/09 → 17/09/09

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10.1007/978-3-642-04277-5_59

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Zeng, Z., Wang, J., & Huang, T. (2009). Global exponential stability of recurrent neural networks with time-dependent switching dynamics. In Artificial Neural Networks - ICANN 2009: 19th International Conference, Proceedings (Vol. 5769 LNCS, pp. 583-592). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5769 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-04277-5_59

Zeng, Zhigang ; Wang, Jun ; Huang, Tingwen. / Global exponential stability of recurrent neural networks with time-dependent switching dynamics. Artificial Neural Networks - ICANN 2009: 19th International Conference, Proceedings. Vol. 5769 LNCS Springer Verlag, 2009. pp. 583-592 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Global exponential stability of recurrent neural networks with time-dependent switching dynamics",

abstract = "In this paper, the switching dynamics of recurrent neural networks are studied. Sufficient conditions on global exponential stability with an arbitrary switching law or a dwell time switching law and the estimates of Lyapunov exponent are obtained. The obtained results can be used to analyze and synthesize a family of continuous-time configurations with the switching between the configurations. Specially, the obtained results are new and efficacious for the switching between the stable and unstable configurations. Finally, simulation results are discussed to illustrate the theoretical results. {\textcopyright} 2009 Springer Berlin Heidelberg.",

author = "Zhigang Zeng and Jun Wang and Tingwen Huang",

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language = "English",

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volume = "5769 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "583--592",

booktitle = "Artificial Neural Networks - ICANN 2009",

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Zeng, Z, Wang, J & Huang, T 2009, Global exponential stability of recurrent neural networks with time-dependent switching dynamics. in Artificial Neural Networks - ICANN 2009: 19th International Conference, Proceedings. vol. 5769 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5769 LNCS, Springer Verlag, pp. 583-592, 19th International Conference on Artificial Neural Networks, ICANN 2009, Limassol, Cyprus, 14/09/09. https://doi.org/10.1007/978-3-642-04277-5_59

Global exponential stability of recurrent neural networks with time-dependent switching dynamics. / Zeng, Zhigang; Wang, Jun; Huang, Tingwen.
Artificial Neural Networks - ICANN 2009: 19th International Conference, Proceedings. Vol. 5769 LNCS Springer Verlag, 2009. p. 583-592 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5769 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Global exponential stability of recurrent neural networks with time-dependent switching dynamics

AU - Zeng, Zhigang

AU - Wang, Jun

AU - Huang, Tingwen

PY - 2009

Y1 - 2009

N2 - In this paper, the switching dynamics of recurrent neural networks are studied. Sufficient conditions on global exponential stability with an arbitrary switching law or a dwell time switching law and the estimates of Lyapunov exponent are obtained. The obtained results can be used to analyze and synthesize a family of continuous-time configurations with the switching between the configurations. Specially, the obtained results are new and efficacious for the switching between the stable and unstable configurations. Finally, simulation results are discussed to illustrate the theoretical results. © 2009 Springer Berlin Heidelberg.

AB - In this paper, the switching dynamics of recurrent neural networks are studied. Sufficient conditions on global exponential stability with an arbitrary switching law or a dwell time switching law and the estimates of Lyapunov exponent are obtained. The obtained results can be used to analyze and synthesize a family of continuous-time configurations with the switching between the configurations. Specially, the obtained results are new and efficacious for the switching between the stable and unstable configurations. Finally, simulation results are discussed to illustrate the theoretical results. © 2009 Springer Berlin Heidelberg.

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U2 - 10.1007/978-3-642-04277-5_59

DO - 10.1007/978-3-642-04277-5_59

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 3642042767

SN - 9783642042768

VL - 5769 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 583

EP - 592

BT - Artificial Neural Networks - ICANN 2009

PB - Springer Verlag

T2 - 19th International Conference on Artificial Neural Networks, ICANN 2009

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ER -

Zeng Z, Wang J, Huang T. Global exponential stability of recurrent neural networks with time-dependent switching dynamics. In Artificial Neural Networks - ICANN 2009: 19th International Conference, Proceedings. Vol. 5769 LNCS. Springer Verlag. 2009. p. 583-592. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-04277-5_59

Global exponential stability of recurrent neural networks with time-dependent switching dynamics

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