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Journal of Systems Engineering and Electronics 2010年第4期21卷 688-697页

作者： p.balasubramaniam G.Nagamani Department of Mathematics Gandhigram Rural University

The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying *** parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belongs to a given interval,which means that the lower and upper bounds of interval time-varying delays are *** constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique,new delay-dependent passivity conditions are derived in terms of linear matrix inequalities（LMIs）.Finally,numerical examples are given to show the less conservatism of the proposed conditions.

关键词： linear matrix inequality(LMI) stochastic neural network passivity interval time-varying delay Lyapunov method.

Linear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay

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Chinese physics B 2011年第4期20卷 16-26页

作者： S.Lakshmanan p.balasubramaniam Department of Mathematics Gandhigram Rural University

This paper studies the problem of linear matrix inequality （LMI） approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional, some inequality techniques and stochastic stability theory, new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism, which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method.

关键词： delay-dependent stability linear matrix inequality Lyapunov-Krasovskii functional stochastic neural networks

Delay-dependent Robust Stabilization and H_∞ Control for Uncertain Stochastic T-S Fuzzy Systems with Discrete Interval and Distributed Time-varying Delays

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International Journal of Automation and computing 2013年第1期10卷 18-31页

作者： p.balasubramaniam T.Senthilkumar Department of Mathematics Gandhigram Rural University

In this paper, delay-dependent robust stabilization and H∞ control for uncertain stochastic Takagi-Sugeno （T-S） fuzzy systems with discrete interval and distributed time-varying delays are discussed. The purpose of the robust stochastic stabilization problem is to design a memoryless state feedback controller such that the closed-loop system is mean-square asymptotically stable for all admissible uncertainties. In the robust H∞ control problem, in addition to the mean-square asymptotic stability requirement, a prescribed H∞ performance is required to be achieved. Sufficient conditions for the solvability of these problems are proposed in terms of a set of linear matrix inequalities （LMIs） and solving these LMIs, a desired controller can be obtained. Finally, two numerical examples are given to illustrate the effectiveness and less conservativeness of our results over the existing ones.

关键词： Robust stability robust H∞ control stochastic fuzzy systems distributed delay linear matrix inequality (LMI)

Stability of nonlinearly-perturbed systems with time varying delay using LMIs

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Journal of Systems Engineering and Electronics 2013年第6期24卷 1011-1018页

作者： S.Jeeva Sathya Theesar p.balasubramaniam Institute for Mathematical Sciences University of Malaya Department of Mathematics Gandhigram Rural Institute-Deemed University

This paper studies delay dependent robust stability and the stabilization problem of nonlinear perturbed systems with time varying delay. A new set of sufficient conditions for the stability of open as well as close loop systems are obtained in the sense of Lyapunov-Krasovskii. To reduce the conservatism, the work exploits the idea of splitting the delay interval into multiple equal regions so that less information on the time delay can be imposed to derive the results. The derived criterion not only improves the upper bounds of the time delay but also does not require the derivative of the delay to be known at prior. Easily testable sufficient criteria are presented in terms of linear matrix inequalities. It is shown that the derived conditions are very less conservative while comparing the maximum allowable upper bound of delay with the existing results in literature.

关键词： delay dependent stability time-varying delay nonlinear perturbations

Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays

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Chinese physics B 2012年第4期21卷 586-596页

作者： p.balasubramaniam M.Kalpana R.Rakkiyappan Department of Mathematics Gandhigram Rural Institute-Deemed UniversityGandhigram-624302TamilnaduIndia Department of Mathematics Bharathiar UniversityCoimbatore-641046TamilnaduIndia

Fuzzy cellular neural networks （FCNNs） are special kinds of cellular neural networks （CNNs）. Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality （LMI） approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov-Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method.

关键词： asymptotic stability chaos fuzzy cellular neural networks linear matrix inequalities synchronization

Controllability of Fractional Order Stochastic Differential Inclusions with Fractional Brownian Motion in Finite Dimensional Space

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IEEE/CAA Journal of Automatica Sinica 2016年第4期3卷 400-410页

作者： T.Sathiyaraj p.balasubramaniam Department of Mathematics The Gandhigram Rural Institute-Deemed University

In this paper,sufficient conditions are formulated for controllability of fractional order stochastic differential inclusions with fractional Brownian motion(f Bm) via fixed point theorems,namely the Bohnenblust-Karlin fixed point theorem for the convex case and the Covitz-Nadler fixed point theorem for the nonconvex *** controllability Grammian matrix is defined by using Mittag-Leffler matrix ***,a numerical example is presented to illustrate the efficiency of the obtained theoretical results.

关键词： Controllability fractional Brownian motion fractional order derivatives Mittag-Leffler function stochastic differential inclusions

State estimation for neural neutral-type networks with mixed time-varying delays and Markovian jumping parameters

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Chinese physics B 2012年第10期21卷 29-37页

作者： S.Lakshmanan Ju H.park H.Y.Jung p.balasubramaniam Department of Information and Communication Engineering/Electrical Engineering Yeungnam University214-1 Dae-DongKyongsan 712-749Republic of Korea Department of Mathematics Gandhigram Rural Institute-Deemed UniversityGandhigram - 624 302TamilnaduIndia

This paper is concerned with a delay-dependent state estimator for neutral-type neural networks with mixed timevarying delays and Markovian jumping *** addressed neural networks have a finite number of modes,and the modes may jump from one to another according to a Markov *** construction of a suitable Lyapunov-Krasovskii functional,a delay-dependent condition is developed to estimate the neuron states through available output measurements such that the estimation error system is globally asymptotically stable in a mean *** criterion is formulated in terms of a set of linear matrix inequalities（LMIs）,which can be checked efficiently by use of some standard numerical packages.

关键词： neural networks state estimation neutral delay Markovian jumping parameters

CT Liver Segmentation Based on Fuzzy Cellular Neural Networks and Its Stability

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CT Liver Segmentation Based on Fuzzy Cellular Neural Network...

2012 International Conference on Affective Computing and Intelligent Interaction(ICACII 2012)

作者： p.balasubramaniam M.Kalpana Institute of Mathematical Sciences University of Malaya50603Kuala LumpurMalasiya Department of Mathematics Gandhigram Rural Institute-Deemed UniversityGandhigram-624 302TamilnaduIndia

Computed tomography or Computer tomography（CT） liver images acquired by CT imaging mechanisms often contain uncertainties due to electrical/hot noise,the diversity of the human organ and the partial volume *** uncertainties make boundary segmentations very difficult among different organs appearing in liver *** aim of this paper is to determine a well defined CT liver *** this paper,a simple thresholding（ST） and region of interest（ROI） methods are used with fuzzy cellular neural network（FCNN） to obtain a well defined CT liver ***,the experimental and comparison results are provided to illustrate the effectiveness of our proposed simple technique.

关键词： Image segmentation Simple thresholding Region of interest Fuzzy cellular neural network CT liver image

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腰段脊柱的神经根管

颈腰痛杂志 1985年第1期 22-22页

作者： K.Bose ,p.balasubramaniam ,陈默南,王儒敬贵阳学医院贵阳学医院

神经根管是椎管不可少的一部分。正确了解神经根管的解剖,对诊断和治疗腰腿痛是必需的。作者解剖了10个新鲜男性尸体的脊柱,其平均年龄60岁,6个中国人、4个印度人,确定了腰段脊柱各神经管的入口、行径、出口和毗邻关系,测量了其长度、... 详细信息

神经根管是椎管不可少的一部分。正确了解神经根管的解剖,对诊断和治疗腰腿痛是必需的。作者解剖了10个新鲜男性尸体的脊柱,其平均年龄60岁,6个中国人、4个印度人,确定了腰段脊柱各神经管的入口、行径、出口和毗邻关系,测量了其长度、直径和倾斜度。神经根管与腹股沟管相似,为管状。起于硬膜囊外侧,向下外斜行至椎间孔出口处,此孔可称为管的外环。神经根管的内环呈漏斗形接纳由脊膜囊分出的神经根。管的内侧半后方被黄韧带复盖,前面为椎间盘及椎体。管的外侧半全为骨性,由椎板、关节突间关节、椎弓根和椎间盘构成。

关键词：神经根管椎管骶神经根腰神经根脊柱

Stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks with mixed delays and the Wiener process based on sampled-data control

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Chinese physics B 2013年第7期22卷 564-573页

作者： M. Kalpana p. balasubramaniam Department of Mathematics Gandhigram Rural InstituteDeemed University

We investigate the stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks （MJFCNNs） with discrete, unbounded distributed delays, and the Wiener process based on sampled-data control using the linear matrix inequality （LMI） approach. The Lyapunov–Krasovskii functional combined with the input delay approach as well as the free-weighting matrix approach is employed to derive several sufficient criteria in terms of LMIs to ensure that the delayed MJFCNNs with the Wiener process is stochastic asymptotical synchronous. Restrictions （e.g., time derivative is smaller than one） are removed to obtain a proposed sampled-data controller. Finally, a numerical example is provided to demonstrate the reliability of the derived results.

关键词： stochastic asymptotical synchronization fuzzy cellular neural networks chaotic Markovian jumping parameters sampled-data control