Design of H∞ performance state estimator for static neural networks with time-varying delay

2019 ◽  
Vol 364 ◽  
pp. 203-208 ◽  
Author(s):  
Guoqiang Tan ◽  
Zhanshan Wang
Keyword(s):  
Neural Networks ◽  
Time Varying ◽  
State Estimator ◽  
Time Varying Delay ◽  
Static Neural Networks ◽  
Varying Delay

scholarly journals Improved Generalized H2 Filtering for Static Neural Networks with Time-Varying Delay via Free-Matrix-Based Integral Inequality

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hui-Jun Yu ◽  
Yong He ◽  
Min Wu
Keyword(s):  
Neural Networks ◽  
Integral Inequality ◽  
Time Varying ◽  
Globally Asymptotically Stable ◽  
Time Varying Delay ◽  
Static Neural Networks ◽  
Error System ◽  
Delay Dependent ◽  
Varying Delay ◽  
Filtering Problem

This paper focuses on the generalized H2 filtering of static neural networks with a time-varying delay. The aim of this problem is to design a full-order filter such that the filtering error system is globally asymptotically stable with guaranteed H2 performance index. By constructing an augmented Lyapunov-Krasovskii functional and applying the free-matrix-based integral inequality to estimate its derivative, an improved delay-dependent condition for the generalized H2 filtering problem is established in terms of LMIs. Finally, a numerical example is presented to show the effectiveness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document