Dissipativity analysis for discrete-time fuzzy neural networks with leakage and time-varying delays

2016 ◽  
Vol 175 ◽  
pp. 579-584 ◽  
Author(s):  
Zhiqiang Ma ◽  
Guanghui Sun ◽  
Di Liu ◽  
Xing Xing
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Fuzzy Neural Networks ◽  
Time Varying ◽  
Fuzzy Neural ◽  
Dissipativity Analysis

scholarly journals Dissipativity analysis of stochastic fuzzy neural networks with randomly occurring uncertainties using delay dividing approach

2019 ◽  
Vol 24 (4) ◽  
Author(s):  
Sundaram Senthilraj ◽  
Ramachandran Raja ◽  
Jinde Cao ◽  
Habib M. Fardoun
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Fuzzy Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Randomly Occurring Uncertainties ◽  
Fuzzy Neural ◽  
Delay Partitioning ◽  
Dissipativity Analysis

This paper focuses on the problem of delay-dependent robust dissipativity analysis for a class of stochastic fuzzy neural networks with time-varying delay. The randomly occurring uncertainties under consideration are assumed to follow certain mutually uncorrelated Bernoulli-distributed white noise sequences. Based on the Itô's differential formula, Lyapunov stability theory, and linear matrix inequalities techniques, several novel sufficient conditions are derived using delay partitioning approach to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties. It is shown, by comparing with existing approaches, that the delay-partitioning projection approach can largely reduce the conservatism of the stability results. Numerical examples are constructed to show the effectiveness of the theoretical results.

Type-II fuzzy neural networks with self-organizing recurrent intervals for dynamic time-varying system identification

2011 ◽  
Vol 19 (6) ◽  
pp. 1406-1413 ◽  
Author(s):  
李迪 LI Di ◽  
陈向坚 CHEN Xiang-jian ◽  
续志军 XU Zhi-jun ◽  
杨帆 YANG Fan ◽  
牛文达 NIU Wen-da
Keyword(s):  
Neural Networks ◽  
System Identification ◽  
Fuzzy Neural Networks ◽  
Time Varying ◽  
Type Ii ◽  
Fuzzy Neural ◽  
Dynamic Time ◽  
Self Organizing

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

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
YaJun Li ◽  
Quanxin Zhu
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Sufficient Conditions ◽  
Fuzzy Neural Networks ◽  
Linear Matrix ◽  
Mixed Delays ◽  
The Novel ◽  
Free Weight ◽  
Fuzzy Neural ◽  
The Stability

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.

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