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Mean square exponential stability of generalized stochastic neural networks with time‐varying delays
Author(s) -
Yu Jianjiang,
Zhang Kanjian,
Fei Shumin
Publication year - 2009
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.144
Subject(s) - exponential stability , stability (learning theory) , mean square , mathematics , artificial neural network , control theory (sociology) , stochastic neural network , class (philosophy) , exponential function , linear matrix inequality , control (management) , computer science , mathematical optimization , recurrent neural network , nonlinear system , mathematical analysis , artificial intelligence , physics , quantum mechanics , machine learning
In this paper, the mean square exponential stability problem is dealt with a class of uncertain generalized stochastic neural networks with time‐varying delays. By introducing a new Lyapunov‐Krasovskii functional, improved delay‐dependent stability criteria are established in terms of linear matrix inequalities. The activation functions are assumed to be of more general descriptions, which generalize and improve those earlier methods. Finally, a numerical example is given to show that our results are less conservative and more efficient than the existing stability criteria. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society