Original Articles: Noise suppress exponential growth for hybrid Hopfield neural networks

Authors:
Song Zhu;Yi Shen;Guici Chen
Affiliations:
College of Sciences, China University of Mining and Technology, Xuzhou 221116, China and Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ...;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China and College of Sciences, Wuhan University of Science and Technology, Wuhan 430081, ...
Venue:
Mathematics and Computers in Simulation
Year:
2012

Citing 13
Cited 0

Stabilization of a class of nonlinear stochastic systems

Nonlinear Analysis: Theory, Methods & Applications
Global stability analysis in delayed Hopfield neural network models

Neural Networks
Global asymptotic stability of Hopfield neural network involving distributed delays

Neural Networks
Exponential periodicity and stability of delayed neural networks

Mathematics and Computers in Simulation
Global stability analysis of a class of delayed cellular neural networks

Mathematics and Computers in Simulation
Stabilization and destabilization of hybrid systems of stochastic differential equations

Automatica (Journal of IFAC)
Global robust stability of interval neural networks with multiple time-varying delays

Mathematics and Computers in Simulation
Robust stability of stochastic delayed additive neural networks with Markovian switching

Neural Networks
Brief paper: Almost sure exponential stabilisation of stochastic systems by state-feedback control

Automatica (Journal of IFAC)
Improved delay-dependent exponential stability criteria for discrete-time recurrent neural networks with time-varying delays

Neurocomputing
State estimation for jumping recurrent neural networks with discrete and distributed delays

Neural Networks
Improved conditions for global exponential stability of recurrent neural networks with time-varying delays

IEEE Transactions on Neural Networks
Noise-Induced Stabilization of the Recurrent Neural Networks With Mixed Time-Varying Delays and Markovian-Switching Parameters

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abstract: In this paper, we show that noise can transform a hybrid neural networks, whose solution may grow exponentially, into a new stochastic one, whose solution grows at most polynomially. In other words, we reveal that noise can suppress the exponential growth in hybrid Hopfield neural networks.