Padmanabhan S
@rnsit.ac.in
Assistant Professor
RNS Institute of Technology
RESEARCH INTERESTS
Stability Analysis, Mathematical Inequalities, Synchronization of Complex Dynamical networks, Functional Analysis, Graph Theory, ...
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
S. Ravi Chandra, S. Padmanabhan, V. Umesha, M. Syed Ali, Grienggrai Rajchakit, and Anuwat Jirawattanpanit
Springer Science and Business Media LLC
AbstractThe sampling data control of bidirectional associative memory (BAM) neural network with leakage delay is considered in this article. The BAM model is viewed as a mixed delay that combines a distributed delay, a discrete delay that varies over time, and a delay in the leaking period. The sampling system is then converted to a continuous time-delay system using an input delay method. In order to get adequate conditions in the form of linear matrix inequalities(LMIs), we build a new Lyapunov-Krasovskii Functional (LKF) in conjunction with the free weight matrix approach. Finally, a simulation results are given to show the efficiency of the theoretical approach.
Sreenivasa Reddy Perla, S. Padmanabhan, and V. Lokesha
New York Business Global LLC
In this paper, we investigated the Schur geometric convexity of related function for Holders Inequality by using majorization inequality theory and some applications are established.
K. S. Anand, J. Yogambigai, G. A. Harish Babu, M. Syed Ali, and S. Padmanabhan
Springer Science and Business Media LLC
This article discusses the synchronization problem of singular neutral complex dynamical networks (SNCDN) with distributed delay and Markovian jump parameters via pinning control. Pinning control strategies are designed to make the singular neutral complex networks synchronized. Some delay-dependent synchronization criteria are derived in the form of linear matrix inequalities based on a modified Lyapunov-Krasovskii functional approach. By applying the Lyapunov stability theory, Jensen’s inequality, Schur complement, and linear matrix inequality technique, some new delay-dependent conditions are derived to guarantee the stability of the system. Finally, numerical examples are presented to illustrate the effectiveness of the obtained results.
K.S. Anand, G.A. Harish Babu, M. Syed Ali, and S. Padmanabhan
Elsevier BV
Abstract This paper of finite-time synchronization for Markovian jumping complex dynamical frameworks with hybrid couplings is studied. A state feedback control is planned for finite-time synchronization of complex frameworks is presented. Sufficient synchronization criteria are proposed in light of the Lyapunov stability theory. A sensible Lyapunov-Krasovskii functional (LKF) is worked with Kronecker products. The desired state feedback controller can be refined by comprehending a plan of linear matrix inequalities (LMIs). Numerical simulation of complex frameworks demonstrates the comprehensiveness and the ampleness of the proposed method.
P. Baskar, S. Padmanabhan, and M. Syed Ali
Informa UK Limited
ABSTRACT In this paper, the problem of stability condition for mixed delayed stochastic neural networks with neutral delay and leakage delay is investigated. A novel Lyapunov functional is constructed with double and triple integral terms. New sufficient conditions are derived to guarantee the global asymptotic stability of the concerned neural network. This paper is more general than the paper by Zhu et al. [Robust stability of Markovian jump stochastic neural networks with time delays in the leakage terms, Neural Process. Lett. 41 (2015), pp. 1–27]. In our paper, we considered both the neutral delay and leakage delay, but the paper by Zhu et al. is not considering the neutral delay. Also we employed triple integrals in the Lyapunov functional which is not used in the paper by Zhu et al. Finally, two numerical examples are provided to show the effectiveness of the theoretical results.
Sreenivasa Reddy Perla and S. Padmanabhan
Springer Science and Business Media LLC
In this paper, we research the Schur convexity, Schur geometric convexity and Schur harmonic convexity of the Bonferroni harmonic mean. Some inequalities identified with the Bonferroni harmonic mean are set up to represent the utilizations of the acquired outcomes.
N. Mohan, R. Ashok Kumar, K. Rajesh, S. Padmanaban, K. Chetan, and M. Akshay Prasad
Elsevier BV
V. Umesha, S. Padmanabhan, P. Baskar and Muhammad Syed Ali
In this paper for neutral delay differential systems, the problem of determining the exponential stability is investigated. Based on the Lyapunov method, we present some useful criteria of exponential stability for the derived systems. The stability criterion is formulated in terms of linear matrix inequality (LMI),which can be easily solved by using the MATLAB LMI toolbox. Numerical examples are included to illustrate the proposed method.
S. Saravanan, V. Umesha, M. Syed Ali, and S. Padmanabhan
Elsevier BV
Abstract This paper is concerned with the problem of an exponential passivity analysis for uncertain neural networks with time-varying delays. By constructing an appropriate Lyapunov–Krasovskii functional and using the weighted integral inequality techniques to estimate its derivative. We established a sufficient criterion such that, for all admissible parameter uncertainties, the neural network is exponentially passive. The derived criteria are expressed in the terms of linear matrix inequalities (LMIs), that can be easily checked by using the standard numerical software. Illustrative examples are presented to demonstrate the effectiveness and usefulness of the proposed results.
P. BASKAR, S. PADMANABHAN, and M. Syed ALI
Elsevier BV
Abstract In this article, we investigates finite-time H∞ control problem of Markovian jumping neural networks of neutral type with distributed time varying delays. The mathematical model of the Markovian jumping neural networks with distributed delays is established in which a set of neural networks are used as individual subsystems. Finite time stability analysis for such neural networks is addressed based on the linear matrix inequality approach. Numerical examples are given to illustrate the usefulness of our proposed method. The results obtained are compared with the results in the literature to show the conservativeness.