Stability Analysis for Discrete-Time Stochastic Fuzzy Neural Networks with Mixed Delays

Yajun Li; Quanxin Zhu

doi:10.1155/2019/8529053

Stability Analysis for Discrete-Time Stochastic Fuzzy Neural Networks with Mixed Delays

Yajun Li, Quanxin Zhu

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

This paper is concerned with the stability problem of a class of discrete-time stochastic fuzzy neural networks with mixed delays. New Lyapunov-Krasovskii functions are proposed and free weight matrices are introduced. The novel sufficient conditions for the stability of discrete-time stochastic fuzzy neural networks with mixed delays are established in terms of linear matrix inequalities (LMIs). Finally, numerical examples are given to illustrate the effectiveness and benefits of the proposed method.

Original language	English
Article number	8529053
Journal	Mathematical Problems in Engineering
Volume	2019
DOIs	https://doi.org/10.1155/2019/8529053
State	Published - 2019
Externally published	Yes

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title = "Stability Analysis for Discrete-Time Stochastic Fuzzy Neural Networks with Mixed Delays",

abstract = "This paper is concerned with the stability problem of a class of discrete-time stochastic fuzzy neural networks with mixed delays. New Lyapunov-Krasovskii functions are proposed and free weight matrices are introduced. The novel sufficient conditions for the stability of discrete-time stochastic fuzzy neural networks with mixed delays are established in terms of linear matrix inequalities (LMIs). Finally, numerical examples are given to illustrate the effectiveness and benefits of the proposed method.",

author = "Yajun Li and Quanxin Zhu",

note = "Publisher Copyright: {\textcopyright} 2019 YaJun Li and Quanxin Zhu.",

year = "2019",

doi = "10.1155/2019/8529053",

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N2 - This paper is concerned with the stability problem of a class of discrete-time stochastic fuzzy neural networks with mixed delays. New Lyapunov-Krasovskii functions are proposed and free weight matrices are introduced. The novel sufficient conditions for the stability of discrete-time stochastic fuzzy neural networks with mixed delays are established in terms of linear matrix inequalities (LMIs). Finally, numerical examples are given to illustrate the effectiveness and benefits of the proposed method.

AB - This paper is concerned with the stability problem of a class of discrete-time stochastic fuzzy neural networks with mixed delays. New Lyapunov-Krasovskii functions are proposed and free weight matrices are introduced. The novel sufficient conditions for the stability of discrete-time stochastic fuzzy neural networks with mixed delays are established in terms of linear matrix inequalities (LMIs). Finally, numerical examples are given to illustrate the effectiveness and benefits of the proposed method.

UR - https://www.scopus.com/pages/publications/85062283434

U2 - 10.1155/2019/8529053

DO - 10.1155/2019/8529053

M3 - 文章

AN - SCOPUS:85062283434

SN - 1024-123X

VL - 2019

JO - Mathematical Problems in Engineering

JF - Mathematical Problems in Engineering

M1 - 8529053

ER -

Stability Analysis for Discrete-Time Stochastic Fuzzy Neural Networks with Mixed Delays

Abstract

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