Mean Square Exponential Stabilization Analysis of Stochastic Neural Networks with Saturated Impulsive Input
Copyright policy )Mean Square Exponential Stabilization Analysis of Stochastic Neural Networks with Saturated Impulsive Input
The exponential stabilization of stochastic neural networks in mean square sense with saturated impulsive input is investigated in this paper. Firstly, the saturated term is handled by polyhedral representation method. When the impulsive sequence is determined by average impulsive interval, impulsive density and mode-dependent impulsive density, the sufficient conditions for stability are proposed, respectively. Then, the ellipsoid and the polyhedron are used to estimate the attractive domain, respectively. By transforming the estimation of the attractive domain into a convex optimization problem, a relatively optimum domain of attraction is obtained. Finally, a three-dimensional continuous time Hopfield neural network example is provided to illustrate the effectiveness and rationality of our proposed theoretical results.
- Southeast University China (People's Republic of)
saturated impulsive input, Stochastic Processes, Time Factors, average impulsive interval, Exponential stability, stochastic neural networks, attractive domain, Physical Phenomena, Networked control, impulsive density, Impulsive control/observation systems, Neural Networks, Computer, Stochastic stability in control theory, Algorithms
saturated impulsive input, Stochastic Processes, Time Factors, average impulsive interval, Exponential stability, stochastic neural networks, attractive domain, Physical Phenomena, Networked control, impulsive density, Impulsive control/observation systems, Neural Networks, Computer, Stochastic stability in control theory, Algorithms
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