Design of generalized dissipativity state estimator for static neural networks including state time delays and leakage delays

2018 ◽  
Vol 355 (9) ◽  
pp. 3990-4014 ◽  
Author(s):  
R. Manivannan ◽  
Yang Cao
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
State Estimator ◽  
Leakage Delays ◽  
State Time ◽  
Static Neural Networks

Design of state estimator for bidirectional associative memory neural networks with leakage delays

2015 ◽  
Vol 296 ◽  
pp. 263-274 ◽  
Author(s):  
R. Sakthivel ◽  
P. Vadivel ◽  
K. Mathiyalagan ◽  
A. Arunkumar ◽  
M. Sivachitra
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Bidirectional Associative Memory ◽  
State Estimator ◽  
Leakage Delays

Design of H∞ state estimator for delayed static neural networks under hybrid-triggered control and imperfect measurement strategy

2020 ◽  
Vol 357 (17) ◽  
pp. 13231-13257
Author(s):  
Shiyu Dong ◽  
Hong Zhu ◽  
Yuping Zhang ◽  
Shouming Zhong ◽  
Jun Cheng ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Estimator ◽  
Static Neural Networks ◽  
Measurement Strategy

Synchronization analysis for static neural networks with hybrid couplings and time delays

2015 ◽  
Vol 148 ◽  
pp. 288-293 ◽  
Author(s):  
Bonan Huang ◽  
Huaguang Zhang ◽  
Dawei Gong ◽  
Junyi Wang
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Static Neural Networks

scholarly journals Robust H∞ filtering for Markovian jumping static neural networks with time-varying delays

2020 ◽  
Vol 14 ◽  
pp. 174830262093134
Author(s):  
Aodong Zhao ◽  
Nan Zhang ◽  
Maolong Xi ◽  
Jun Sun ◽  
Meiyan Dong
Keyword(s):  
Neural Networks ◽  
Lyapunov Functions ◽  
Time Delays ◽  
Activation Function ◽  
Time Varying ◽  
Markovian Jumping ◽  
Wirtinger Inequality ◽  
Static Neural Networks ◽  
Stochastically Stable ◽  
Conservative Result

The problem of robust H∞ filtering for Markovian jumping static neural networks with time-varying delays is considered in this paper. The effect of the activation function on the time delays is comprehensively considered. Based on Wirtinger inequality, a new inequality is quoted to solve the Lyapunov functions with the double-integral terms. Then, a less conservative result on the robust H∞ filtering is obtained, which guarantees the resulting error systems stochastically stable and satisfies a prescribed H∞ performance index. The effectiveness of the developed results is finally demonstrated by numerical examples.

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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