What is he best known for?
The fields of study he is best known for:
- Control theory
- Mathematical analysis
- Artificial neural network
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.
His most cited work include:
- Finite-time synchronization of fractional-order memristor-based neural networks with time delays (170 citations)
- Exponential input-to-state stability of stochastic Cohen–Grossberg neural networks with mixed delays (162 citations)
- Existence and uniform stability analysis of fractional-order complex-valued neural networks with time delays. (159 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Control theory (81.18%)
- Artificial neural network (47.31%)
- Linear matrix inequality (35.48%)
What were the highlights of his more recent work (between 2017-2021)?
- Control theory (81.18%)
- Artificial neural network (47.31%)
- Nonlinear system (11.83%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- Synchronization of an Inertial Neural Network With Time-Varying Delays and Its Application to Secure Communication (123 citations)
- Persistent impulsive effects on stability of functional differential equations with finite or infinite delay (77 citations)
- Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection (55 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Control theory
- Artificial intelligence
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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