Intermittent Event-triggered Exponential Stabilization for State-Dependent Switched Fuzzy Neural Networks With Mixed Delays

2021 ◽  
pp. 1-1
Author(s):  
Xiaofan Li ◽  
Nikhil R. Pal ◽  
Huiyuan Li ◽  
Tingwen Huang
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Exponential Stabilization ◽  
Mixed Delays ◽  
State Dependent ◽  
Fuzzy Neural ◽  
Event Triggered

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.

scholarly journals Synchronization of Chaotic Delayed Fuzzy Neural Networks under Impulsive and Stochastic Perturbations

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Bing Li ◽  
Qiankun Song
Keyword(s):  
Neural Networks ◽  
Cellular Neural Networks ◽  
Fuzzy Neural Networks ◽  
Itô’S Formula ◽  
Mixed Delays ◽  
Fuzzy Cellular Neural Networks ◽  
Stochastic Perturbations ◽  
Synchronization Problem ◽  
Ito’S Formula ◽  
Fuzzy Neural

The synchronization problem of chaotic fuzzy cellular neural networks with mixed delays is investigated. By an impulsive integrodifferential inequality and the Itô's formula, some sufficient criteria to synchronize the networks under both impulsive and stochastic perturbations are obtained. The example and simulations are given to demonstrate the efficiency and advantages of the proposed results.

Event-Triggered State Estimation for T–S Fuzzy Neural Networks with Stochastic Cyber-Attacks

2019 ◽  
Vol 21 (2) ◽  
pp. 532-544 ◽  
Author(s):  
Jinliang Liu ◽  
Tingting Yin ◽  
Xiangpeng Xie ◽  
Engang Tian ◽  
Shumin Fei
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Fuzzy Neural Networks ◽  
Cyber Attacks ◽  
Fuzzy Neural ◽  
Event Triggered

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

Hotbox level detection of railway vehicle using fuzzy neural networks

2013 ◽  
Vol 33 (9) ◽  
pp. 2566-2569 ◽  
Author(s):  
Zhuanling CUI ◽  
Guoning LI ◽  
Sen LIN
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Railway Vehicle ◽  
Fuzzy Neural
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