Vellore, India
Vellore, India

Thiruvallauvar University is a public university located in the city of Vellore, Tamil Nadu, India. It was established by the government of Tamil Nadu, under the Thiruvalluvar University Act, 2002 in the year 2002. Thiruvallauvar University was named after the Tamil poet and philosopher Thiruvalluvar.It began functioning as the Postgraduate Extension Centre, of University of Madras, at the Fort Campus, Vellore. After bifurcation from the University of Madras, Thiruvallauvar University moved to a new campus at Serkadu, Vellore. Wikipedia.


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Syed Ali M.,Thiruvalluvar University
International Journal of Machine Learning and Cybernetics | Year: 2014

In this paper, stability of stochastic recurrent neural networks with Markovian jumping parameters and time-varying delays is considered. A novel linear matrix inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of Markovian jumping stochastic recurrent neural networks with norm bounded uncertainties and time-varying delays. To reflect the most dynamical behaviors of the system, both parameter uncertainties and stochastic disturbance are considered, where parameter uncertainties enter into all the system matrices, stochastic disturbances are given in the form of a Brownian motion. The stability conditions are derived using Lyapunov-Krasovskii approach, in combined with the LMI techniques. The delay-dependent stability condition is formulated, in which the restriction of the derivative of the time-varying delay should be 1 is removed. Finally, numerical examples are given to demonstrate the correctness of the theoretical results. © 2012 Springer-Verlag.


Soniyapriyadharishni A.K.,Thiruvalluvar University
Research Journal of Pharmaceutical, Biological and Chemical Sciences | Year: 2013

From its very beginning, the potential of extracting valuable knowledge from the Data and the Web has been quite evident. We well know that, Data mining is a technique where we get the extract of raw data and useful information, Whereas, Web mining is the application of data mining techniques to extract knowledge from Web content, structure, and usage. Interest in Data mining and Web mining has grown rapidly in its short existence, both in the research and practitioner communities. This paper provides a brief overview of the accomplishments that "How Data mining is Inter-specific with Web mining" fields both in terms of technologies and applications - and outlines key functions, classification and research directions.


Syed Ali M.,Thiruvalluvar University | Marudai M.,Bharathidasan University
Mathematical and Computer Modelling | Year: 2011

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. © 2011 Elsevier Ltd.


In this paper, global stability of Markovian jumping recurrent neural networks with discrete and distributed delays (MJRNN) is considered. A novel linear matrix inequality (LMI) based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of Markovian jumping recurrent neural networks with discrete and distributed delays. By applying Lyapunov method and some inequality techniques, several sufficient conditions are obtained under which the delayed neural networks are stable. Finally, numerical examples are given to demonstrate the correctness of the theoretical results. © 2014 Elsevier B.V.


Syed Ali M.,Thiruvalluvar University
Iranian Journal of Fuzzy Systems | Year: 2014

In this paper, global robust stability of stochastic impulsive re- current neural networks with time-varying delays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov func- tional theory to guarantee the asymptotic stability of uncertain fuzzy stochas- tic impulsive recurrent neural networks with time-varying delays. The results are related to the size of delay and impulses. Finally, numerical examples and simulations are given to demonstrate the correctness of the theoretical results. © 2014, University of Sistan and Baluchestan. All rights reserved.


In this paper, the global stability of Takagi - Sugeno (TS) uncertain stochastic fuzzy recurrent neural networks with discrete and distributed time-varying delays (TSUSFRNNs) is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of TSUSFRNNs. The proposed stability conditions are demonstrated through numerical examples. Furthermore, the supplementary requirement that the time derivative of time-varying delays must be smaller than one is removed. Comparison results are demonstrated to show that the proposed method is more able to guarantee the widest stability region than the other methods available in the existing literature. © 2011 Chinese Physical Society and IOP Publishing Ltd.


This paper presents the stability analysis for a class of neural networks with time varying delays that are represented by the Takagi - Sugeno (T - S) model. The main results given here focus on the stability criteria using a new Lyapunov functional. New relaxed conditions and new linear matrix inequality-based designs are proposed that outperform the previous results found in the literature. Numerical examples are provided to show that the achieved conditions are less conservative than the existing ones in the literature. © 2012 Chinese Physical Society and IOP Publishing Ltd.


In this paper, the global asymptotic stability problem of Markovian jumping stochastic Cohen - Grossberg neural networks with discrete and distributed time-varying delays (MJSCGNNs) is considered. A novel LMI-based stability criterion is obtained by constructing a new Lyapunov functional to guarantee the asymptotic stability of MJSCGNNs. Our results can be easily verified and they are also less restrictive than previously known criteria and can be applied to Cohen - Grossberg neural networks, recurrent neural networks, and cellular neural networks. Finally, the proposed stability conditions are demonstrated with numerical examples. © 2014 Chinese Physical Society and IOP Publishing Ltd.


Syed Ali M.,Thiruvalluvar University
Acta Mathematica Scientia | Year: 2015

In this paper, global robust stability of uncertain stochastic recurrent neural networks with Markovian jumping parameters is considered. A novel Linear matrix inequality(LMI) based stability criterion is obtained to guarantee the asymptotic stability of uncertain stochastic recurrent neural networks with Markovian jumping parameters.The results are derived by using the Lyapunov functional technique, Lipchitz condition and S-procuture. Finally, numerical examples are given to demonstrate the correctness of the theoretical results. Our results are also compared with results discussed in [31] and [34] to show the effectiveness and conservativeness. © 2015 Wuhan Institute of Physics and Mathematics.


Raja R.,Periyar University | Samidurai R.,Thiruvalluvar University
Journal of the Franklin Institute | Year: 2012

This paper is concerned with the stability analysis problem for a class of delayed stochastic recurrent neural networks with both discrete and distributed time-varying delays. By constructing a suitable Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to ensure the global, robust asymptotic stability for the addressed system in the mean square. The conditions obtained here are expressed in terms of LMIs whose feasibility can be checked easily by MATLAB LMI Control toolbox. In addition, two numerical examples with comparative results are given to justify the obtained stability results. © 2012 The Franklin Institute. All rights reserved.

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