Passivity analysis for discrete-time neural networks with mixed time-delays and randomly occurring quantization effects

This paper investigates the passivity analysis problem for a class of discrete-time neural networks subject to the mixed time-delays and randomly occurring quantization effects. Both the time-varying discrete delays and the infinite distributed time-delays are considered. The phenomenon of randomly...

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Published in:	Neurocomputing (Amsterdam) Vol. 216; pp. 657 - 665
Main Authors:	Zhang, Jie, Ma, Lifeng, Liu, Yurong
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 05.12.2016
Subjects:	Discrete-time neural networks Linear matrix inequality Mixed time delays Passivity Randomly occurring quantization effects Discrete-time neural networks Linear matrix inequality Passivity Randomly occurring quantization effects Mixed time delays
ISSN:	0925-2312, 1872-8286
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Abstract	This paper investigates the passivity analysis problem for a class of discrete-time neural networks subject to the mixed time-delays and randomly occurring quantization effects. Both the time-varying discrete delays and the infinite distributed time-delays are considered. The phenomenon of randomly occurring logarithmic quantization is taken into consideration, which is described by a random sequence obeying the Bernoulli distribution. Sufficient conditions are established, guaranteeing the globally asymptotical stability in the mean square and the strict (Q,S,R)−γ-dissipative property of the considered neural networks. The main results are proposed by virtue of the linear matrix inequality approach that can be easily solved by certain convex optimization algorithms. The obtained methodology is capable of being adopted in the passivity analysis with little modifications. A numerical example is provided to verify the correctness and effectiveness of the exploited methodology.
AbstractList	This paper investigates the passivity analysis problem for a class of discrete-time neural networks subject to the mixed time-delays and randomly occurring quantization effects. Both the time-varying discrete delays and the infinite distributed time-delays are considered. The phenomenon of randomly occurring logarithmic quantization is taken into consideration, which is described by a random sequence obeying the Bernoulli distribution. Sufficient conditions are established, guaranteeing the globally asymptotical stability in the mean square and the strict (Q,S,R)−γ-dissipative property of the considered neural networks. The main results are proposed by virtue of the linear matrix inequality approach that can be easily solved by certain convex optimization algorithms. The obtained methodology is capable of being adopted in the passivity analysis with little modifications. A numerical example is provided to verify the correctness and effectiveness of the exploited methodology.
Author	Ma, Lifeng Liu, Yurong Zhang, Jie
Author_xml	– sequence: 1 givenname: Jie surname: Zhang fullname: Zhang, Jie organization: School of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China – sequence: 2 givenname: Lifeng orcidid: 0000-0002-1839-6803 surname: Ma fullname: Ma, Lifeng email: malifeng@njust.edu.cn organization: School of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China – sequence: 3 givenname: Yurong surname: Liu fullname: Liu, Yurong organization: Department of Mathematics, Yangzhou University, Yangzhou 225002, PR China
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Snippet	This paper investigates the passivity analysis problem for a class of discrete-time neural networks subject to the mixed time-delays and randomly occurring...
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SubjectTerms	Discrete-time neural networks Linear matrix inequality Mixed time delays Passivity Randomly occurring quantization effects
Title	Passivity analysis for discrete-time neural networks with mixed time-delays and randomly occurring quantization effects
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