Control theory, Linear matrix inequality, Exponential stability, Artificial neural network and Fuzzy logic are his primary areas of study. His work on Stability as part of general Control theory study is frequently connected to Bidirectional associative memory, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His biological study spans a wide range of topics, including Sliding mode control, Nonlinear system, Master/slave and Trajectory.
His studies in Exponential stability integrate themes in fields like H control and Stochastic neural network. His work carried out in the field of Artificial neural network brings together such families of science as Finite time, MATLAB and Type. When carried out as part of a general Fuzzy logic research project, his work on Neuro-fuzzy and Fuzzy control system is frequently linked to work in Data control and Sampling, therefore connecting diverse disciplines of study.
Control theory, Artificial neural network, Linear matrix inequality, Exponential stability and Applied mathematics are his primary areas of study. M. Syed Ali has researched Control theory in several fields, including State, Interval, Fuzzy logic and Stability conditions. His studies deal with areas such as Finite time, Bounded function, Mathematical optimization and Linear matrix as well as Artificial neural network.
His Linear matrix inequality research integrates issues from Lyapunov functional, Stochastic neural network, Markovian jumping and Stability theory. His Exponential stability study incorporates themes from Equilibrium point, Recurrent neural network, Stability criterion and Lyapunov stability. His Applied mathematics research includes themes of Uniqueness and Multiple integral.
His primary scientific interests are in Control theory, Artificial neural network, Applied mathematics, Synchronization and Linear matrix inequality. His Control theory study combines topics in areas such as Recurrent neural network, Computational intelligence, Interval and Fuzzy logic. His Artificial neural network research incorporates elements of Passivity, Exponential stability, Lyapunov function and Type.
His Exponential stability research is multidisciplinary, relying on both Exponential function and Differential equation. He combines subjects such as Finite time, Bounded function and Stability conditions with his study of Applied mathematics. His Linear matrix inequality study integrates concerns from other disciplines, such as Event triggered and Lyapunov stability.
M. Syed Ali mainly investigates Control theory, Artificial neural network, Applied mathematics, Fuzzy logic and Synchronization. His study in the field of Linear matrix inequality, Exponential stability and Stability also crosses realms of Weighting. His Artificial neural network research is multidisciplinary, incorporating perspectives in Equilibrium point and Lyapunov function.
His Applied mathematics research focuses on subjects like Bounded function, which are linked to Dynamical systems theory, Nonlinear system and Markovian jump. M. Syed Ali combines subjects such as Control, Markovian jumping, Uniqueness and Inequality with his study of Fuzzy logic. His Interval research is multidisciplinary, incorporating perspectives in Recurrent neural network, Computational intelligence, Measure, State and Observer.
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State estimation of T–S fuzzy delayed neural networks with Markovian jumping parameters using sampled-data control ☆
M. Syed Ali;Nallappan Gunasekaran;Quanxin Zhu.
Fuzzy Sets and Systems (2017)
Stability analysis of uncertain fuzzy Hopfield neural networks with time delays
M. Syed Ali;P. Balasubramaniam.
Communications in Nonlinear Science and Numerical Simulation (2009)
New passivity criteria for memristor-based neutral-type stochastic BAM neural networks with mixed time-varying delays
M. Syed Ali;R. Saravanakumar;Jinde Cao.
Delay-dependent stability criteria of uncertain Markovian jump neural networks with discrete interval and distributed time-varying delays
M. Syed Ali;Sabri Arik;R. Saravanakumar.
Global asymptotic stability of stochastic fuzzy cellular neural networks with multiple time-varying delays
P. Balasubramaniam;M. Syed Ali;Sabri Arik.
Expert Systems With Applications (2010)
Finite-time boundedness, L2-gain analysis and control of Markovian jump switched neural networks with additive time-varying delays
M. Syed Ali;S. Saravanan;Jinde Cao.
Nonlinear Analysis: Hybrid Systems (2017)
Stability of Markovian jumping recurrent neural networks with discrete and distributed time-varying delays
M. Syed Ali.
Synchronization of master-slave markovian switching complex dynamical networks with time-varying delays in nonlinear function via sliding mode control
M. Syed Ali;J. Yogambigai;Jinde Cao;Jinde Cao.
Acta Mathematica Scientia (2017)
Robust exponential stability of uncertain fuzzy Cohen--Grossberg neural networks with time-varying delays
P. Balasubramaniam;M. Syed Ali.
Fuzzy Sets and Systems (2010)
Decentralized event-triggered synchronization of uncertain Markovian jumping neutral-type neural networks with mixed delays
Sibel Senan;M. Syed Ali;R. Vadivel;Sabri Arik.
Neural Networks (2017)
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