A. Chandrasekar

According to our database, A. Chandrasekar authored at least 18 papers between 2013 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Analysis of Markovian Jump Stochastic Cohen-Grossberg BAM Neural Networks with Time Delays for Exponential Input-to-State Stability.

T. Radhika, A. Chandrasekar, V. Vijayakumar, Quanxin Zhu

Neural Process. Lett., December, 2023

2022

Further Results on Input-to-State Stability of Stochastic Cohen-Grossberg BAM Neural Networks with Probabilistic Time-Varying Delays.

A. Chandrasekar, T. Radhika, Quanxin Zhu

Neural Process. Lett., 2022

State Estimation for Genetic Regulatory Networks with Two Delay Components by Using Second-Order Reciprocally Convex Approach.

A. Chandrasekar, T. Radhika, Quanxin Zhu

Neural Process. Lett., 2022

2016

Stochastic sampled data robust stabilisation of T-S fuzzy neutral systems with randomly occurring uncertainties and time-varying delays.

R. Rakkiyappan, A. Chandrasekar, S. Lakshmanan

Int. J. Syst. Sci., 2016

Synchronization and periodicity of coupled inertial memristive neural networks with supremums.

R. Rakkiyappan, E. Udhaya Kumari, A. Chandrasekar, Ramasamy Krishnasamy

Neurocomputing, 2016

Effects of bounded and unbounded leakage time-varying delays in memristor-based recurrent neural networks with different memductance functions.

A. Chandrasekar, R. Rakkiyappan, Xiaodi Li

Neurocomputing, 2016

Impulsive controller design for exponential synchronization of delayed stochastic memristor-based recurrent neural networks.

A. Chandrasekar, R. Rakkiyappan

Neurocomputing, 2016

Robust stability analysis of stochastic neural networks with Markovian jumping parameters and probabilistic time-varying delays.

Chandrasekar Pradeep, Arunachalam Chandrasekar, Rangasamy Murugesu, Rajan Rakkiyappan

Complex., 2016

2015

Passivity and Passification of Memristor-Based Recurrent Neural Networks With Additive Time-Varying Delays.

Rajan Rakkiyappan, A. Chandrasekar, Jinde Cao

IEEE Trans. Neural Networks Learn. Syst., 2015

Impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach.

A. Chandrasekar, R. Rakkiyappan, Jinde Cao

Neural Networks, 2015

Exponential stability for markovian jumping stochastic BAM neural networks with mode-dependent probabilistic time-varying delays and impulse control.

R. Rakkiyappan, A. Chandrasekar, Shanmugam Lakshmanan, Ju H. Park

Complex., 2015

2014

Synchronization of memristor-based recurrent neural networks with two delay components based on second-order reciprocally convex approach.

A. Chandrasekar, R. Rakkiyappan, Jinde Cao, Shanmugam Lakshmanan

Neural Networks, 2014

Stability of stochastic neural networks of neutral type with Markovian jumping parameters: A delay-fractioning approach.

R. Rakkiyappan, Quanxin Zhu, A. Chandrasekar

J. Frankl. Inst., 2014

Stochastic stability of Markovian jump BAM neural networks with leakage delays and impulse control.

Quanxin Zhu, R. Rakkiyappan, A. Chandrasekar

Neurocomputing, 2014

Exponential stability of Markovian jumping stochastic Cohen-Grossberg neural networks with mode-dependent probabilistic time-varying delays and impulses.

R. Rakkiyappan, A. Chandrasekar, Shanmugam Lakshmanan, Ju H. Park

Neurocomputing, 2014

Exponential synchronization of Markovian jumping neural networks with partly unknown transition probabilities via stochastic sampled-data control.

A. Chandrasekar, R. Rakkiyappan, Fathalla A. Rihan, Shanmugam Lakshmanan

Neurocomputing, 2014

State estimation of memristor-based recurrent neural networks with time-varying delays based on passivity theory.

R. Rakkiyappan, A. Chandrasekar, Shanmugam Lakshmanan, Ju H. Park

Complex., 2014

2013

Effects of leakage time-varying delays in Markovian jump neural networks with impulse control.

R. Rakkiyappan, A. Chandrasekar, Shanmugam Lakshmanan, Ju H. Park, Ho Y. Jung

Neurocomputing, 2013