NDLI: Robust Exponential Stability for Discrete-Time Uncertain BAM Neural Networks Markovian Jump System with Time Delays

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2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)

The Relationship between Decision Type and Knowledge Management Preferences

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The Empirical Study on the Vitality Index of Organization for the Taiwanese Hospitality

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Robust Exponential Stability for Discrete-Time Uncertain BAM Neural Networks Markovian Jump System with Time Delays

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2008 3rd International Conference on Innovative Computing Information and Control

Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007)

First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06)

Robust Exponential Stability for Discrete-Time Uncertain BAM Neural Networks Markovian Jump System with Time Delays

Content Provider	IEEE Xplore Digital Library
Author	Jiqing Qiu Kunfeng Lu
Copyright Year	2009
Abstract	This paper is concerned with the problem of robust exponential stability for discrete-time BAM neural networks with mode-dependent time delays and Markovian jump parameters, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, the global exponential stability is investigated. The time delay varies in an interval and depends on the mode of operation. A new Markov process as discrete-time, discrete-state Markov process is considered. A numerical example illustrates the effectiveness of the method.
Starting Page	103
Ending Page	106
File Size	170375
Page Count	4
File Format	PDF
ISBN	9781424455430
e-ISBN	9781424455447
DOI	10.1109/ICICIC.2009.322
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-07
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robust control Linear systems Sufficient conditions Robust stability Delay effects Neural networks Neurons Markov processes Control systems Magnesium compounds
Content Type	Text
Resource Type	Article

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