Fixed/predefined-time synchronization of fuzzy neural networks with stochastic perturbations

2021 ◽  
pp. 111596
Author(s):  
Mairemunisa Abudusaimaiti ◽  
Abdujelil Abdurahman ◽  
Haijun Jiang ◽  
Cheng Hu
Keyword(s):  
Neural Networks ◽  
Time Synchronization ◽  
Fuzzy Neural Networks ◽  
Stochastic Perturbations ◽  
Fuzzy Neural

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.

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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