مقاله Stochastic stability of discrete-time uncertain recurrent neural networks with Markovian jumping and time-varying delays : تی پی بین

خلاصه مقاله

In this paper, the problem of robust exponential stability analysis of uncertain discrete-time recurrent neural networks with Markovian jumping and time-varying delays is studied. By employing the Lyapunov functional and linear matrix inequality (LMI) approach, a new sufficient criterion is proposed for the global robust exponential stability of discrete-time recurrent neural networks which contain uncertain parameters and Markovian jumping parameters. The obtained stability criterion is characterized in terms of linear matrix inequalities (LMIs) and can be easily checked by utilizing the efficient LMI toolbox. Two numerical examples are presented to show the effectiveness and conservativeness of the proposed method.

نویسندگان

M. Syed Ali [ Department of Mathematics, Thiruvalluvar University, Vellore-632 106, Tamilnadu, India ]

M. Marudai [ Department of Mathematics, Bharathidasan University, Trichy-620 024, Tamilnadu, India ]

کلمات کلیدی

Discrete time recurrent neural networks Linear matrix inequality Markovian jumping parameters Time varying delays

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مشاهده مقالات

Mathematical and Computer Modelling 2011

Mathematical and Computer Modeling provides a medium of exchange for the diverse disciplines utilizing mathematical or computer modeling as either a theoretical or working tool. Equal attention is given to the mechanics, methodology and theory of modeling with an attempt to advocate either mathematical or computer modeling, or a combination of the two, in an integrative form. While the unifying aspect of the journal is its adherence to quantitative models, it seeks diversity by being concerned with a variety of disciplines, including engineering, biological, medical, environmental, social, behavioral and other sciences .Both theoretical and applied works which employ mathematical or computer modeling will be considered for publication. Papers dealing with experiments shall be considered when the results are presented as an integral part of the modeling process.

حوزه های تحت پوشش : engineering, biological, medical, environmental, social