Global exponential stability of almost periodic solution of delayed neural networks with discontinuous activations

Authors:
Sitian Qin;Xiaoping Xue;Peng Wang
Affiliations:
Department of Mathematics, Harbin Institute of Technology at Weihai, Weihai 264209, China;Department of Mathematics, Harbin Institute of Technology, Harbin 150001, China;Department of Mathematics, Harbin Institute of Technology, Harbin 150001, China
Venue:
Information Sciences: an International Journal
Year:
2013

Citing 22
Cited 2

Letters: On the almost periodic solution of generalized Hopfield neural networks with time-varying delays

Neurocomputing
Almost periodic dynamics of a class of delayed neural networks with discontinuous activations

Neural Computation
Neural networks letter: On periodic solutions of neural networks via differential inclusions

Neural Networks
Global Exponential Stability and Global Convergence in Finite Time of Neural Networks with Discontinuous Activations

Neural Processing Letters
Global stability analysis of a general class of discontinuous neural networks with linear growth activation functions

Information Sciences: an International Journal
Artificial neural network approach for solving fuzzy differential equations

Information Sciences: an International Journal
Multistability and new attraction basins of almost-periodic solutions of delayed neural networks

IEEE Transactions on Neural Networks
Existence and global attractivity of almost periodic solutions for delayed high-ordered neural networks

Neurocomputing
Exponential stability on stochastic neural networks with discrete interval and distributed delays

IEEE Transactions on Neural Networks
Non-affine nonlinear adaptive control of decentralized large-scale systems using neural networks

Information Sciences: an International Journal
Global stability analysis in Cohen-Grossberg neural networks with delays and inverse Hölder neuron activation functions

Information Sciences: an International Journal
Dynamical behavior of a class of nonsmooth gradient-like systems

Neurocomputing
Common asymptotic behavior of solutions and almost periodicity for discontinuous, delayed, and impulsive neural networks

IEEE Transactions on Neural Networks
Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay

Information Sciences: an International Journal
New passivity analysis for neural networks with discrete and distributed delays

IEEE Transactions on Neural Networks
Global convergence of neural networks with mixed time-varying delays and discontinuous neuron activations

Information Sciences: an International Journal
Synchronization control of a class of memristor-based recurrent neural networks

Information Sciences: an International Journal
Multistability analysis for a general class of delayed Cohen-Grossberg neural networks

Information Sciences: an International Journal
Existence and global exponential stability of almost periodic solution for cellular neural networks with variable coefficients and time-varying delays

IEEE Transactions on Neural Networks
Global exponential stability and global convergence in finite time of delayed neural networks with infinite gain

IEEE Transactions on Neural Networks
Passivity Analysis for Discrete-Time Stochastic Markovian Jump Neural Networks With Mixed Time Delays

IEEE Transactions on Neural Networks
Delay-Dependent Stability Analysis for Switched Neural Networks With Time-Varying Delay

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.07

Visualization

Abstract

In this paper, we study the existence, uniqueness and stability of almost periodic solution for the class of delayed neural networks. The neural network considered in this paper employs the activation functions which are discontinuous monotone increasing and (possibly) unbounded. Under a new sufficient condition, we prove that the neural network has a unique almost periodic solution, which is globally exponentially stable. Moreover, the obtained conclusion is applied to prove the existence and stability of periodic solution (or equilibrium point) for delayed neural networks with periodic coefficients (or constant coefficients). We also give some illustrative numerical examples to show the effectiveness of our results.