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Delay-dependent robust stability criteria for stochastic neural networks of neutral-type with interval time-varying delay and linear fractional uncertainties†

Published online by Cambridge University Press:  04 September 2014

GUOQUAN LIU ,
SIMON X. YANG ,
YI CHAI  and
WEI FU
GUOQUAN LIU
Affiliation:
College of Automation, Chongqing University, Chongqing 400044, China School of Mechanical and Electronic Engineering, East China Institute of Technology, Nanchang, 330013, China Email: guoquanliu1982@hotmail.com
SIMON X. YANG
Affiliation:
College of Automation, Chongqing University, Chongqing 400044, China School of Engineering, University of Guelph, Guelph, Ontario, N1G2W1Canada Email: xianyi@yahoo.com
YI CHAI
Affiliation:
College of Automation, Chongqing University, Chongqing 400044, China State Key Laboratory of Power Transmission Equipment and System Security and New Technology of Engineering, Chongqing University, Chongqing 400044, China Email: yi_chai@yahoo.com.cn
WEI FU
Affiliation:
College of Automation, Chongqing University, Chongqing 400044, China Email: linefw@163.com
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Abstract

In this paper, we investigate the problem of robust stability for a class of delayed neural networks of neutral-type with linear fractional uncertainties. The activation functions are assumed to be unbounded, non-monotonic and non-differentiable, and the delay is assumed to be time-varying and belonging to a given interval, which means that the lower and upper bounds of the interval time-varying delay are available. By constructing a general form of the Lyapunov–Krasovskii functional, and using the linear matrix inequality (LMI) approach, we derive several delay-dependent stability criteria in terms of LMI. Finally, we give a number of examples to illustrate the effectiveness of the proposed method.

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Footnotes

†

This work was supported by both the National Natural Science Foundation (No. 60974090) and the Fundamental Research Funds for the Central Universities (No. CDJXS11172237).

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