Delay-dependent state estimation criteria for uncertain neural networks via parameter-dependent Lyapunov functional

2018 ◽  
Author(s):  
Qian Ye ◽  
X.Y. Lou
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Lyapunov Functional ◽  
Uncertain Neural Networks ◽  
Delay Dependent ◽  
Parameter Dependent ◽  
Dependent State

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.

Delay-dependent state estimation of neural networks with mixed time-varying delays

2014 ◽  
Vol 129 ◽  
pp. 392-400 ◽  
Author(s):  
S. Lakshmanan ◽  
K. Mathiyalagan ◽  
Ju H. Park ◽  
R. Sakthivel ◽  
Fathalla A. Rihan
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Time Varying ◽  
Delay Dependent ◽  
Dependent State

Delay-Dependent State Estimation for Delayed Neural Networks

2006 ◽  
Vol 17 (4) ◽  
pp. 1077-1081 ◽  
Author(s):  
Y. He ◽  
Q.-G. Wang ◽  
M. Wu ◽  
C. Lin
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Delayed Neural Networks ◽  
Delay Dependent ◽  
Dependent State

Improved Results on State Estimation for Uncertain Takagi-Sugeno Fuzzy Stochastic Neural Networks with Time-Varying Delays

2017 ◽  
Vol 18 (6) ◽  
pp. 495-505
Author(s):  
Yajun Li ◽  
Feiqi Deng ◽  
Jingzhao Li
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Estimation Error ◽  
Fuzzy Rule ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
State Estimators ◽  
Delay Dependent ◽  
Takagi Sugeno ◽  
Dependent State

AbstractThe delay-dependent state estimation problem for Takagi-Sugeno fuzzy stochastic neural networks with time-varying delays is considered in this paper. We aim to design state estimators to estimate the network states such that the dynamics of the estimation error systems are guaranteed to be exponentially stable in the mean square. Both fuzzy-rule-independent and the fuzzy-rule-dependent state estimators are designed. Delay-dependent sufficient conditions are presented to guarantee the existence of the desired state estimators for the fuzzy stochastic neural networks. Finally, two numerical examples demonstrate that the proposed approaches are effective.

scholarly journals Improved Results onH∞State Estimation of Static Neural Networks with Time Delay

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Bin Wen ◽  
Hui Li ◽  
Shouming Zhong
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
State Estimator ◽  
Static Neural Networks ◽  
Exponentially Stable ◽  
Error System ◽  
Delay Dependent ◽  
Dependent State

This paper studies the problem ofH∞state estimation for a class of delayed static neural networks. The purpose of the problem is to design a delay-dependent state estimator such that the dynamics of the error system is globally exponentially stable and a prescribedH∞performance is guaranteed. Some improved delay-dependent conditions are established by constructing augmented Lyapunov-Krasovskii functionals (LKFs). The desired estimator gain matrix can be characterized in terms of the solution to LMIs (linear matrix inequalities). Numerical examples are provided to illustrate the effectiveness of the proposed method compared with some existing results.

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