Control theory, Linear matrix inequality, Artificial neural network, Exponential stability and Interval are his primary areas of study. His work deals with themes such as Fuzzy logic and Stability conditions, which intersect with Control theory. His research in Linear matrix inequality intersects with topics in Lyapunov functional, Stochastic stability, Stability criterion and Stochastic neural network.
His work on Recurrent neural network as part of general Artificial neural network study is frequently connected to Synchronization, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. Pagavathigounder Balasubramaniam has researched Exponential stability in several fields, including Equilibrium point, Differential equation, Lyapunov function, MATLAB and Numerical analysis. His study focuses on the intersection of Interval and fields such as Matrix with connections in the field of Markov chain.
His primary scientific interests are in Control theory, Linear matrix inequality, Artificial neural network, Exponential stability and Fuzzy logic. His studies in Control theory integrate themes in fields like Stability criterion and Interval. His studies examine the connections between Linear matrix inequality and genetics, as well as such issues in Stability theory, with regards to State.
His work on Feedforward neural network as part of general Artificial neural network study is frequently linked to Synchronization, bridging the gap between disciplines. His Exponential stability research is multidisciplinary, incorporating elements of Equilibrium point, Recurrent neural network and Numerical stability. His Nonlinear system research is multidisciplinary, relying on both Controllability and Mathematical analysis.
Control theory, Applied mathematics, Fractional calculus, Controllability and Nonlinear system are his primary areas of study. The study incorporates disciplines such as Artificial neural network and Bidirectional associative memory in addition to Control theory. His biological study spans a wide range of topics, including Lyapunov functional and Reaction–diffusion system.
His Applied mathematics study incorporates themes from Fixed point, Exponential stability and Uniqueness. Null and Bounded function is closely connected to Linear system in his research, which is encompassed under the umbrella topic of Controllability. The Linear matrix inequality study combines topics in areas such as Discrete time neural networks, State, Fuzzy logic and Stability conditions.
His main research concerns Control theory, Fractional calculus, Artificial neural network, Linear matrix inequality and Synchronization. The Exponential stability and Nonlinear system research Pagavathigounder Balasubramaniam does as part of his general Control theory study is frequently linked to other disciplines of science, such as Synchronization of chaos and Leakage, therefore creating a link between diverse domains of science. His Exponential stability research incorporates themes from Lyapunov stability and Markov chain.
His study in Fractional calculus is interdisciplinary in nature, drawing from both Dynamical systems theory, Fixed-point theorem, Hilbert space and Resolvent. In most of his Artificial neural network studies, his work intersects topics such as Differential equation. Pagavathigounder Balasubramaniam combines topics linked to Rose with his work on Linear matrix inequality.
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Existence and global stability analysis of equilibrium of fuzzy cellular neural networks with time delay in the leakage term under impulsive perturbations
Xiaodi Li;R. Rakkiyappan;P. Balasubramaniam.
Journal of The Franklin Institute-engineering and Applied Mathematics (2011)
Synchronization of an Inertial Neural Network With Time-Varying Delays and Its Application to Secure Communication
Shanmugam Lakshmanan;Mani Prakash;Chee Peng Lim;Rajan Rakkiyappan.
IEEE Transactions on Neural Networks (2018)
Existence, uniqueness and stability analysis of recurrent neural networks with time delay in the leakage term under impulsive perturbations
Xiaodi Li;Xilin Fu;Xilin Fu;P. Balasubramaniam;R. Rakkiyappan.
Nonlinear Analysis-real World Applications (2010)
Global exponential stability results for neutral-type impulsive neural networks
R. Rakkiyappan;P. Balasubramaniam;Jinde Cao.
Nonlinear Analysis-real World Applications (2010)
Improved results on robust stability of neutral systems with mixed time-varying delays and nonlinear perturbations
S. Lakshmanan;T. Senthilkumar;P. Balasubramaniam.
Applied Mathematical Modelling (2011)
Delay-dependent stability of neutral systems with time-varying delays using delay-decomposition approach
P. Balasubramaniam;R. Krishnasamy;R. Rakkiyappan.
Applied Mathematical Modelling (2012)
Delay-dependent asymptotic stability for stochastic delayed recurrent neural networks with time varying delays☆
R. Rakkiyappan;Pagavathigounder Balasubramaniam.
Applied Mathematics and Computation (2008)
Letters: New global exponential stability results for neutral type neural networks with distributed time delays
R. Rakkiyappan;P. Balasubramaniam.
Stability analysis of uncertain fuzzy Hopfield neural networks with time delays
M. Syed Ali;P. Balasubramaniam.
Communications in Nonlinear Science and Numerical Simulation (2009)
Controllability for neutral stochastic functional differential inclusions with infinite delay in abstract space
P. Balasubramaniam;S.K. Ntouyas.
Journal of Mathematical Analysis and Applications (2006)
Profile was last updated on December 6th, 2021.
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