H∞ filter design for delayed static neural networks with Markovian switching and randomly occurred nonlinearity

被引：0

作者：

Cheng, Yaling ^{[1
]}

Hua, Mingang ^{[1
]}

Cheng, Pei ^{[2
]}

Yao, Fengqi ^{[3
]}

Fei, Juntao ^{[1
]}

机构：

[1] Hohai Univ, Coll Internet Things Engn, Changzhou 213022, Peoples R China

[2] Anhui Univ, Sch Math Sci, Hefei 230601, Anhui, Peoples R China

[3] Anhui Univ Technol, Sch Elect & Informat Engn, Maanshan 243000, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2018年 / 9卷 / 06期

基金：

高等学校博士学科点专项科研基金; 中国国家自然科学基金;

关键词：

Filter design; Static neural networks; Markovian switching; Randomly occurred nonlinearity; Linear matrix inequalities; TIME-VARYING DELAYS; STABILITY ANALYSIS; EXPONENTIAL STABILITY; STOCHASTIC STABILITY; DISCRETE; INTERVAL; CRITERIA;

D O I：

10.1007/s13042-016-0613-0

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The paper is concerned with the problem of H-infinity filter design for delayed static neural networks with Markovian switching and randomly occurred nonlinearity. The random phenomenon is described in terms of a Bernoulli stochastic variable. Based on the reciprocally convex approach, a lower bound lemma is proposed to handle the double- and triple-integral terms in the time derivative of the Lyapunov function. Finally, the optimal performance index is obtained via solving linear matrix inequalities(LMIs). The result is not only less conservative but the time derivative of the time delay can be greater than one. Numerical examples with simulation results are provided to illustrate the effectiveness of the developed results.

引用

页码：903 / 915

页数：13

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