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.

DESIGN OF STATE ESTIMATOR FOR SWITCHED HOPFIELD NEURAL NETWORKS WITH TIME-DELAY

2011 ◽  
Vol 20 (04) ◽  
pp. 657-666
Author(s):  
CHOON KI AHN
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Estimation Error ◽  
Hopfield Neural Networks ◽  
Linear Matrix ◽  
Asymptotically Stable ◽  
State Estimator ◽  
Error System ◽  
Delay Dependent ◽  
Dependent State

In this paper, the delay-dependent state estimation problem for switched Hopfield neural networks with time-delay is investigated. Based on the Lyapunov–Krasovskii stability theory, a new delay-dependent state estimator for switched Hopfield neural networks is established to estimate the neuron states through available output measurements such that the estimation error system is asymptotically stable. The gain matrix of the proposed estimator is characterized in terms of the solution to a linear matrix inequality (LMI), which can be checked readily by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed state estimator.

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 H∞ Filtering for Neural Networks with Time Delay

2014 ◽  
Vol 511-512 ◽  
pp. 875-879 ◽  
Author(s):  
Ya Jun Li ◽  
Yan Nong Liang
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Filter Design ◽  
Linear Matrix ◽  
Globally Asymptotically Stable ◽  
H Infinity ◽  
Decomposition Approach ◽  
Delay Decomposition Approach ◽  
Error System ◽  
Delay Dependent

The H{infinity} filter design problem of recurrent neural networks with time delay is considered. Based on delay decomposition approach, the delay-dependent condition is derived to ensure that the filtering error system is globally asymptotically stable with a guaranteed performance. And the design of such a filter can be solved by the linear matrix inequality. A numerical example is provided to demonstrate that the developed approach is efficient.

scholarly journals LMI Approach to Stability Analysis of Cohen-Grossberg Neural Networks withp-Laplace Diffusion

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Xiongrui Wang ◽  
Ruofeng Rao ◽  
Shouming Zhong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Lmi Approach ◽  
Exponentially Stable ◽  
New Criterion ◽  
Diffusion Simulation ◽  
And Diffusion ◽  
Laplace Diffusion

The nonlinearp-Laplace diffusion (p>1) was considered in the Cohen-Grossberg neural network (CGNN), and a new linear matrix inequalities (LMI) criterion is obtained, which ensures the equilibrium of CGNN is stochastically exponentially stable. Note that, ifp=2,p-Laplace diffusion is just the conventional Laplace diffusion in many previous literatures. And it is worth mentioning that even ifp=2, the new criterion improves some recent ones due to computational efficiency. In addition, the resulting criterion has advantages over some previous ones in that both the impulsive assumption and diffusion simulation are more natural than those of some recent literatures.

scholarly journals Delay-Dependent Stability Analysis for Recurrent Neural Networks with Time-Varying Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Shu Lv ◽  
Junkang Tian ◽  
Shouming Zhong
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Stability Criteria ◽  
Activation Functions ◽  
New Class ◽  
Delay Dependent ◽  
Delay Dependent Stability

This paper concerns the problem of delay-dependent stability criteria for recurrent neural networks with time varying delays. By taking more information of states and activation functions as augmented vectors, a new class of the Lyapunov functional is proposed. Then, some less conservative stability criteria are obtained in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are given to illustrate the effectiveness of the proposed method.

scholarly journals Delay-Dependent RobustH∞Filtering of the Takagi-Sugeno Fuzzy Stochastic Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Ze Li ◽  
Xin-Hao Yang
Keyword(s):  
Stochastic Systems ◽  
Design Method ◽  
Linear Matrix ◽  
Mean Square ◽  
Matrix Inequalities ◽  
Parameter Uncertainties ◽  
Mean Square Stability ◽  
Error System ◽  
Delay Dependent ◽  
Takagi Sugeno

This paper is concerned with the problem of the robustH∞filtering for the Takagi-Sugeno (T-S) fuzzy stochastic systems with bounded parameter uncertainties. For a given T-S fuzzy stochastic system, this paper focuses on the stochastically mean-square stability of the filtering error system and theH∞performance level of the output error and the disturbance input. The design method for delay-dependent filter is developed based on linear matrix inequalities. Finally, the effectiveness of the proposed methods is substantiated with an illustrative example.

scholarly journals Switched Exponential State Estimation and Robust Stability for Interval Neural Networks with Discrete and Distributed Time Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Hongwen Xu ◽  
Huaiqin Wu ◽  
Ning Li
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
State Estimation ◽  
Time Delays ◽  
Estimation Error ◽  
Linear Matrix ◽  
Robust Exponential Stability ◽  
Interval Neural Networks ◽  
Distributed Time Delays ◽  
Error System

The interval exponential state estimation and robust exponential stability for the switched interval neural networks with discrete and distributed time delays are considered. Firstly, by combining the theories of the switched systems and the interval neural networks, the mathematical model of the switched interval neural networks with discrete and distributed time delays and the interval estimation error system are established. Secondly, by applying the augmented Lyapunov-Krasovskii functional approach and available output measurements, the dynamics of estimation error system is proved to be globally exponentially stable for all admissible time delays. Both the existence conditions and the explicit characterization of desired estimator are derived in terms of linear matrix inequalities (LMIs). Moreover, a delay-dependent criterion is also developed, which guarantees the robust exponential stability of the switched interval neural networks with discrete and distributed time delays. Finally, two numerical examples are provided to illustrate the validity of the theoretical results.

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