Robust exponential stabilization of positive uncertain switched neural networks with actuator saturation and sensor faults

In this paper, the problem of global robust exponential stabilization for a class of neural networks with reaction-diffusion terms and time-varying delays which covers the Hopfield neural networks and cellular neural networks is investigated. A feedback control gain matrix is derived to achieve the global robust exponential stabilization of the neural networks by using the Lyapunov stability theory, and the stabilization condition can be verified if a certain Hamiltonian matrix with no eigenvalues on the imaginary axis. This condition can avoid solving an algebraic Riccati equation. Finally, a numerical simulation illustrates the effectiveness of the results.

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Robust Exponential Stabilization of Stochastic Delay Interval Recurrent Neural Networks with Distributed Parameters and Markovian Jumping by Using Periodically Intermittent Control

Abstract and Applied Analysis ◽

10.1155/2014/842976 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Junhao Hu ◽

Yunjian Peng ◽

Yan Li

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Sufficient Conditions ◽

Exponential Stabilization ◽

Intermittent Control ◽

Markovian Jumping ◽

Stochastic Delay ◽

Periodically Intermittent Control ◽

Distributed Parameters ◽

Robust Exponential Stabilization

We consider a class of stochastic delay recurrent neural networks with distributed parameters and Markovian jumping. It is assumed that the coefficients in these neural networks belong to the interval matrices. Several sufficient conditions ensuring robust exponential stabilization are derived by using periodically intermittent control and Lyapunov functional. The obtained results are very easy to verify and implement, and improve the existing results. Finally, an example with numerical simulations is given to illustrate the presented criteria.

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Robust Exponential Stabilization of Stochastic Neural Networks with Markovian Switching

2008 International Conference on Apperceiving Computing and Intelligence Analysis ◽

10.1109/icacia.2008.4769981 ◽

2008 ◽

Author(s):

Guo-Cai Jiang ◽

Shou-Ming Zhong

Keyword(s):

Neural Networks ◽

Markovian Switching ◽

Exponential Stabilization ◽

Stochastic Neural Networks ◽

Robust Exponential Stabilization

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Adaptive state estimation of Markov switched neural networks driven by Lévy noise

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219869472 ◽

2019 ◽

Vol 42 (2) ◽

pp. 330-336

Author(s):

Dongbing Tong ◽

Qiaoyu Chen ◽

Wuneng Zhou ◽

Yuhua Xu

Keyword(s):

Neural Networks ◽

State Estimation ◽

Sufficient Conditions ◽

Lyapunov Stability Theory ◽

Lévy Noise ◽

Linear Matrix ◽

Delayed Neural Networks ◽

Switched Neural Networks ◽

Inequality Technique ◽

Levy Noise

This paper proposes the [Formula: see text]-matrix method to achieve state estimation in Markov switched neural networks with Lévy noise, and the method is very distinct from the linear matrix inequality technique. Meanwhile, in light of the Lyapunov stability theory, some sufficient conditions of the exponential stability are derived for delayed neural networks, and the adaptive update law is obtained. An example verifies the condition of state estimation and confirms the effectiveness of results.

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