Effects of leakage delays and impulsive control in dissipativity analysis of Takagi–Sugeno fuzzy neural networks with randomly occurring uncertainties

2017 ◽  
Vol 354 (8) ◽  
pp. 3574-3593 ◽  
Author(s):  
R. Samidurai ◽  
S. Senthilraj ◽  
Quanxin Zhu ◽  
R. Raja ◽  
Wei Hu
Keyword(s):  
Neural Networks ◽  
Impulsive Control ◽  
Fuzzy Neural Networks ◽  
Leakage Delays ◽  
Randomly Occurring Uncertainties ◽  
Fuzzy Neural ◽  
Takagi Sugeno ◽  
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.

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.

Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks

2012 ◽  
Vol 3 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Sevil Ahmed ◽  
Nikola Shakev ◽  
Andon Topalov ◽  
Kostadin Shiev ◽  
Okyay Kaynak
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Takagi Sugeno
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