Exponential Convergence Rate Estimation for a Class of BAM Neural Networks with Time-Delays

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This paper is concerned with the exponential stability analysis problem for a class of neutral bidirectional associative memory (BAM) neural networks with parameter uncertainties and mixed time-delays where the parameter uncertainties are norm-bounded and the mixed time-delays involve discrete, distributed and neutral time-delays. By utilizing free-weighting matrix method and an appropriately constructed Lyapunov-Krasovskii Functional, some nove delay-dependent and decay-rate dependent exponential stability criteria are derived in the terms of linear matrix inequalities (LMIs). Meanwhile, the maximum allowable decay rate can be estimated based on the obtained results. Two numerical examples are presented to demonstrate the effectiveness of the proposed method.

Info:

Periodical:

Advanced Materials Research (Volumes 143-144)

Edited by:

H. Wang, B.J. Zhang, X.Z. Liu, D.Z. Luo, S.B. Zhong

Pages:

707-711

DOI:

10.4028/www.scientific.net/AMR.143-144.707

Citation:

J. D. Yu "Exponential Convergence Rate Estimation for a Class of BAM Neural Networks with Time-Delays", Advanced Materials Research, Vols. 143-144, pp. 707-711, 2011

Online since:

October 2010

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$35.00

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