Robust State Estimation for Delayed Neural Networks with Stochastic Parameter Uncertainties

Stability and Robust H∞ Control for Time-Delayed Systems with Parameter Uncertainties and Stochastic Disturbances

1921 Copyright c The Korean Institute of Electrical Engineers This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Stability and Robust H∞ Cont...

Robust Synchronization Criterion for Coupled Stochastic Discrete-Time Neural Networks with Interval Time-Varying Delays, Leakage Delay, and Parameter Uncertainties

and Applied Analysis 3 Theneuron activation functions,g p (y p (⋅)) (p = 1, . . . , n), are assumed to be nondecreasing, bounded, and globally Lipschitz; that is, l − p ≤ g p (ξ p ) − g p (ξ q ) ξ p − ξ q ≤ l + p , ∀ξ p , ξ q ∈ R, ξ p ̸ = ξ q , (5) where l− p and l+ p are constant values. For simplicity, in stability analysis of the network (1), the equilibrium point y∗ = [y∗ 1 , . . . , y ∗ n ]...

Stochastic delayed neural networks

Robust State Estimation for Multi-delayed Neural Networks: An LMI Approach

The robust state estimation problem is studied in this paper for a class of neural networks with multiple time-varying delays and norm-bounded parameter uncertainties. The problem is to estimate the neuron states through available measured outputs such that for all admissible time-delays and parameter uncertainties, the dynamics of the estimation error is globally stable. A sufficient condition...

Robust stability of stochastic fuzzy impulsive recurrent neural networks with\ time-varying delays

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...