His primary areas of investigation include Control theory, Artificial neural network, Linear matrix inequality, Exponential stability and Differential equation. His Control theory research is multidisciplinary, incorporating perspectives in Recurrent neural network and Upper and lower bounds. Rajan Rakkiyappan has included themes like Control theory and Interval in his Upper and lower bounds study.
His Artificial neural network study combines topics from a wide range of disciplines, such as Stability, Time delays and Type. His Exponential stability research incorporates elements of Equilibrium point and Fuzzy logic. His research integrates issues of Applied mathematics and Stability theory in his study of Differential equation.
Rajan Rakkiyappan focuses on Control theory, Artificial neural network, Linear matrix inequality, Exponential stability and Applied mathematics. His Control theory research integrates issues from Upper and lower bounds and Multiple integral. As a part of the same scientific family, Rajan Rakkiyappan mostly works in the field of Artificial neural network, focusing on Stability and, on occasion, Mathematical optimization.
His research in Linear matrix inequality intersects with topics in Stability theory, Bounded function, Interval, Fuzzy logic and MATLAB. His Exponential stability course of study focuses on Equilibrium point and Uniqueness. His work deals with themes such as Class, State and Discrete time and continuous time, which intersect with Applied mathematics.
His main research concerns Control theory, Artificial neural network, Nonlinear system, Applied mathematics and Event triggered. The various areas that Rajan Rakkiyappan examines in his Control theory study include Multiple integral, Fuzzy logic and Stability conditions. His study focuses on the intersection of Stability conditions and fields such as Lyapunov function with connections in the field of Linear matrix inequality.
His work carried out in the field of Artificial neural network brings together such families of science as Piecewise linear function, Chaotic, Chaotic map and Differential equation. The Stability theory research Rajan Rakkiyappan does as part of his general Nonlinear system study is frequently linked to other disciplines of science, such as Transmission time, therefore creating a link between diverse domains of science. He combines subjects such as Quaternion and Delay differential equation with his study of Applied mathematics.
His scientific interests lie mostly in Control theory, Differential equation, Multiple integral, Event triggered and Artificial neural network. His specific area of interest is Control theory, where Rajan Rakkiyappan studies Nonlinear system. His Differential equation research includes elements of Development, Current and Applied mathematics.
His Multiple integral study combines topics in areas such as Auxiliary function, Actuator and Inequality. His Event triggered research incorporates themes from Exponential stability, Quantization and Markov chain. His biological study spans a wide range of topics, including Piecewise linear function, Chaotic, Chaotic map, Stability theory and Secure communication.
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Finite-time synchronization of fractional-order memristor-based neural networks with time delays
G. Velmurugan;R. Rakkiyappan;Jinde Cao.
Neural Networks (2016)
Exponential input-to-state stability of stochastic Cohen–Grossberg neural networks with mixed delays
Quanxin Zhu;Jinde Cao;R. Rakkiyappan.
Nonlinear Dynamics (2015)
Existence and uniform stability analysis of fractional-order complex-valued neural networks with time delays.
R. Rakkiyappan;Jinde Cao;G. Velmurugan.
IEEE Transactions on Neural Networks (2015)
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)
Finite-time stability analysis of fractional-order complex-valued memristor-based neural networks with time delays
R. Rakkiyappan;G. Velmurugan;Jinde Cao;Jinde Cao.
Nonlinear Dynamics (2014)
Impulsive controller design for exponential synchronization of chaotic neural networks with mixed delays
Xiaodi Li;R. Rakkiyappan.
Communications in Nonlinear Science and Numerical Simulation (2013)
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)
Exponential H ∞ filtering analysis for discrete-time switched neural networks with random delays using sojourn probabilities
JinDe Cao;R. Rakkiyappan;K. Maheswari;A. Chandrasekar.
Science China-technological Sciences (2016)
Dissipativity analysis of memristor-based complex-valued neural networks with time-varying delays
Xiaodi Li;R. Rakkiyappan;G. Velmurugan.
Information Sciences (2015)
Profile was last updated on December 6th, 2021.
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