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The problem of robust exponential stability analysis for uncertain stochastic neural networks is investigated based on Lyapunov stability theory. The parametric uncertainties in the neural networks satisfy the Frobenius norm-bounded conditions. The exogenous disturbance and stochastic perturbation functions satisfy the Liptistz conditions. Based on linear matrix inequality approach, the sufficient...
This paper is concerned with an adaptive state estimation problem for a class of continuous-time nonlinear stochastic systems with unknown constant parameters. These nonlinear systems have a linear-in-parameter (affine) structure and the nonlinearity is assumed to be bounded in a Lipschitz-like manner. Using stochastic counterparts of Lyapunov stability theory, we present adaptive state and parameter...
The robust exponential stability of uncertain neural networks with time delay is analyzed based on Lyapunov stability theory. The nonlinear uncertain perturbation in the neural networks satisfies the cone bounded condition. By choosing suitable Lyapunov functional, several sufficient conditions on robust stability and robust exponential stability of uncertain neural networks with time delays are given...
The robust non-fragile H∞ control problem is investigated for uncertain stochastic time-delay systems containing sector nonlinearities. The time delays exist in state, control and stochastic noise simultaneously. The parameter uncertainties in the systems are assumed to satisfy norm-bounded condition. A stochastic robust stabilizable criterion is presented in terms of a group of linear matrix inequalities...
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