Reduced-Order Filtering of Delayed Static Neural Networks With Markovian Jumping Parameters

被引：23

作者：

Huang, He ^{[1
]}

Huang, Tingwen ^{[2
]}

Cao, Yang ^{[3
]}

机构：

[1] Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China

[2] Texas A&M Univ Qatar, Doha 5825, Qatar

[3] Univ Hong Kong, Dept Mech Engn, Hong Kong, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2018年 / 29卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Markovian jumping parameters; mode-dependent time delays; performance analysis; reduced-order filters; static neural networks (SNNs); DEPENDENT H-INFINITY; STABILITY ANALYSIS; INTEGRAL-INEQUALITIES; DISCRETE; SYSTEMS; STABILIZATION; HIERARCHY;

D O I：

10.1109/TNNLS.2018.2806356

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The reduced-order filtering problems are investigated in this paper for static neural networks with Markovian jumping parameters and mode-dependent time-varying delays. By fully making use of integral inequalities, the designs of reduced-order H-infinity and L-2 - L-infinity filters are discussed. The proper gain matrices of filters and the optimal performance indices are efficiently obtained by resolving corresponding convex optimization problems with the constraints of linear matrix inequalities. It is verified that the computational complexity for the reduced-order filter design is significantly reduced when compared with the full-order one. Furthermore, the nonfragile reduced-order filtering problems are also resolved in this paper. Two examples with simulation results are presented to demonstrate the feasibility and application of the established results.

引用

页码：5606 / 5618

页数：13

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