Area of research
Computer Networks and Communications · Statistical and Nonlinear Physics
Research interest
Research topics from publications: Multistability and Stabilization of Fractional-Order Competitive Neural Networks With Unbounded Time-Varying Delays; Multistability of delayed fractional-order competitive neural networks; Multistability of Fractional-Order Neural Networks With Unbounded Time-Varying Delays; Multistability and instability analysis of recurrent neural networks with time-varying delays; Multiple Lagrange Stability Under Perturbation for Recurrent Neural Networks With Time-Varying Delays; Multiple $\psi$ -Type Stability of Cohen–Grossberg Neural Networks With Both Time-Varying Discrete Delays and Distributed Delays; Multistability of recurrent neural networks with time-varying delays and nonincreasing activation function. Representative work: This article investigates the multistability and stabilization of fractional-order competitive neural networks (FOCNNs) with unbounded time-varying delays. By utilizing the monotone operator, several sufficient conditions of the coexistence of equilibrium points (EPs) are obtained for FOCNNs with concave-convex activation functions. And then, the multiple μ -stability of delayed FOCNNs is derived by the analytical method. Meanwhile, several comparisons with existing work are shown, which implies that the derived results cover the inverse-power stability and Mittag-Leffler stability as special cases. Moreover, the criteria on the stabilization of FOCNNs with uncertainty are established by des This article addresses the multistability and attraction of fractional-order neural networks (FONNs) with unbounded time-varying delays. Several sufficient conditions are given to ensure the coexistence of equilibrium points (EPs) of FONNs with concave-convex activation functions. Moreover, by exploiting the analytical method and the property of the Mittag-Leffler function, it is shown that the multiple Mittag-Leffler stability of delayed FONNs is derived and the obtained criteria do not depend on differentiable time-varying delays. In particular, the criterion of the Mittag-Leffler stability can be simplified to M-matrix. In addition, the estimation of attraction basin of delayed FONNs is s
Multistability and Stabilization of Fractional-Order Competitive Neural Networks With Unbounded Time-Varying Delays
Multistability of delayed fractional-order competitive neural networks
Multistability of Fractional-Order Neural Networks With Unbounded Time-Varying Delays
Multiple Lagrange Stability Under Perturbation for Recurrent Neural Networks With Time-Varying Delays
Multiple <inline-formula> <tex-math notation="LaTeX">$\psi$ </tex-math> </inline-formula>-Type Stability of Cohen–Grossberg Neural Networks With Both Time-Varying Discrete Delays and Distributed Delays
Multistability and instability analysis of recurrent neural networks with time-varying delays
Multistability of recurrent neural networks with time-varying delays and nonincreasing activation function