Delay-dependent stability and dissipativity analysis of generalized neural networks with Markovian jump parameters and two delay components

2016 ◽  
Vol 353 (9) ◽  
pp. 2137-2158 ◽  
Author(s):  
Guoliang Chen ◽  
Jianwei Xia ◽  
Guangming Zhuang
Keyword(s):  
Neural Networks ◽  
Markovian Jump ◽  
Markovian Jump Parameters ◽  
Generalized Neural Networks ◽  
Delay Dependent ◽  
Delay Dependent Stability ◽  
Dissipativity Analysis

scholarly journals Further Robust Dissipativity Analysis of Uncertain Stochastic Generalized Neural Networks With Markovian Jump Parameters

2020 ◽  
Author(s):  
Pharunyou Chanthorn ◽  
Grienggrai Rajchakit ◽  
Jenjira Thipcha ◽  
Chanikan Emharuethai ◽  
Ramalingam Sriraman
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Markovian Jump ◽  
Parameter Uncertainties ◽  
Markovian Jumping ◽  
Practical Applications ◽  
Markovian Jump Parameters ◽  
Stochastic Disturbance ◽  
Generalized Neural Networks ◽  
Dissipativity Analysis

This paper analyzes the robust dissipativity of uncertain stochastic generalized neural networks (USGNNs) with Markovian jumping parameters and time-varying delays. In practical applications most of the systems refer to uncertainties, hence, the norm-bounded parameter uncertainties and stochastic disturbance are considered. Then, by constructing an appropriate Lyapunov-Krasovskii functional (LKF) and by employing integral inequalities LMI-based sufficient conditions of the considered systems are established. Numerical simulations are given to show the merit of the presented results.

Dissipativity analysis of delayed stochastic generalized neural networks with Markovian jump parameters

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Grienggrai Rajchakit ◽  
Ramalingam Sriraman ◽  
Rajendran Samidurai
Keyword(s):  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Markovian Jump ◽  
Stochastic Perturbations ◽  
Practical Applications ◽  
Markovian Jump Parameters ◽  
Generalized Neural Networks ◽  
System Information ◽  
Dissipativity Analysis

Abstract This article discusses the dissipativity analysis of stochastic generalized neural network (NN) models with Markovian jump parameters and time-varying delays. In practical applications, most of the systems are subject to stochastic perturbations. As such, this study takes a class of stochastic NN models into account. To undertake this problem, we first construct an appropriate Lyapunov–Krasovskii functional with more system information. Then, by employing effective integral inequalities, we derive several dissipativity and stability criteria in the form of linear matrix inequalities that can be checked by the MATLAB LMI toolbox. Finally, we also present numerical examples to validate the usefulness of the results.

scholarly journals Stability Analysis of Stochastic Markovian Jump Neural Networks with Different Time Scales and Randomly Occurred Nonlinearities Based on Delay-Partitioning Projection Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Jianmin Duan ◽  
Manfeng Hu ◽  
Yongqing Yang ◽  
Liuxiao Guo
Keyword(s):  
Neural Networks ◽  
Time Scales ◽  
Linear Matrix ◽  
Markovian Jump ◽  
Projection Approach ◽  
Delay Dependent ◽  
Delay Partitioning ◽  
Delay Dependent Stability ◽  
Different Time Scales ◽  
Markovian Jump Neural Networks

In this paper, the mean square asymptotic stability of stochastic Markovian jump neural networks with different time scales and randomly occurred nonlinearities is investigated. In terms of linear matrix inequality (LMI) approach and delay-partitioning projection technique, delay-dependent stability criteria are derived for the considered neural networks for cases with or without the information of the delay rates via new Lyapunov-Krasovskii functionals. We also obtain that the thinner the delay is partitioned, the more obviously the conservatism can be reduced. An example with simulation results is given to show the effectiveness of the proposed approach.

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