Memory-based State Estimation of T–S Fuzzy Markov Jump Delayed Neural Networks with Reaction–Diffusion Terms

2019 ◽  
Vol 50 (3) ◽  
pp. 2529-2546 ◽  
Author(s):  
Xiaona Song ◽  
Jingtao Man ◽  
Zhumu Fu ◽  
Mi Wang ◽  
Junwei Lu
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Reaction Diffusion ◽  
Markov Jump ◽  
Delayed Neural Networks

Extended dissipative-based state estimation for Markov jump coupled neural networks with reaction-diffusion terms

2021 ◽  
pp. 014233122110282
Author(s):  
Dongxiao Hu ◽  
Xiaona Song ◽  
Xingru Li
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Dynamic Error ◽  
Signal Transmission ◽  
Reaction Diffusion ◽  
Linear Matrix ◽  
Markov Jump ◽  
Memory Controller ◽  
Coupled Neural Networks ◽  
Error System

This work mainly concentrates on the state estimation for Markov jump coupled neural networks (MJCNNs) with reaction-diffusion terms, in which the memory controller is employed. First, the considered MJCNNs model is introduced, and the dynamic error system can be obtained based on the proposed state estimator. Then, a memory controller that involves constant signal transmission delay is designed. Moreover, by Lyapunov functional method, inequality technique and Kronecker product law, a novel stable and extended dissipative analysis criteria can be established to ensure that the stability of the error system the error system. Meanwhile, the controller gains can be obtained by solving linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the developed method.

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

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.

scholarly journals Robust State Estimation for Delayed Neural Networks with Stochastic Parameter Uncertainties

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
M. J. Park ◽  
O. M. Kwon ◽  
Ju H. Park ◽  
S. M. Lee ◽  
E. J. Cha
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Linear Matrix ◽  
Parameter Uncertainties ◽  
Delayed Neural Networks ◽  
Stochastic Parameter ◽  
Mean And Variance ◽  
Delay Dependent ◽  
Designing Method ◽  
Dependent State

This paper considers the problem of delay-dependent state estimation for neural networks with time-varying delays and stochastic parameter uncertainties. It is assumed that the parameter uncertainties are affected by the environment which is changed with randomly real situation, and its stochastic information such as mean and variance is utilized in the proposed method. By constructing a newly augmented Lyapunov-Krasovskii functional, a designing method of estimator for neural networks is introduced with the framework of linear matrix inequalities (LMIs) and a neural networks model with stochastic parameter uncertainties which have not been introduced yet. Two numerical examples are given to show the improvements over the existing ones and the effectiveness of the proposed idea.

Generalised state estimation of Markov jump neural networks based on the Bessel–Legendre inequality

2019 ◽  
Vol 13 (9) ◽  
pp. 1284-1290 ◽  
Author(s):  
Hao Shen ◽  
Shiyu Jiao ◽  
Jianwei Xia ◽  
Ju H. Park ◽  
Xia Huang
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Markov Jump

Event-triggered Non-fragile State Estimation for Discrete Nonlinear Markov Jump Neural Networks with Sensor Failures

2019 ◽  
Vol 17 (5) ◽  
pp. 1131-1140 ◽  
Author(s):  
Jianning Li ◽  
Zhujian Li ◽  
Yufei Xu ◽  
Kaiyang Gu ◽  
Wendong Bao ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Markov Jump ◽  
Fragile State ◽  
Sensor Failures ◽  
Event Triggered
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