Sampled-data filtering of Takagi–Sugeno fuzzy neural networks with interval time-varying delays

2017 ◽  
Vol 316 ◽  
pp. 69-81 ◽  
Author(s):  
Eylem Yucel ◽  
M. Syed Ali ◽  
N. Gunasekaran ◽  
Sabri Arik
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Time Varying ◽  
Sampled Data ◽  
Data Filtering ◽  
Interval Time ◽  
Fuzzy Neural ◽  
Takagi Sugeno

A DELAY-DEPENDENT APPROACH TO ROBUST PASSIVITY ANALYSIS FOR TAKAGI-SUGENO FUZZY UNCERTAIN RECURRENT NEURAL NETWORKS WITH MIXED INTERVAL TIME-VARYING DELAYS

2013 ◽  
Vol 21 (03) ◽  
pp. 415-433 ◽  
Author(s):  
K. H. TSENG ◽  
J. S. H. TSAI ◽  
C. Y. LU
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Fuzzy Neural Networks ◽  
Linear Matrix ◽  
Uncertain Parameters ◽  
Time Varying ◽  
Interval Time ◽  
Passivity Analysis ◽  
Fuzzy Neural ◽  
Delay Dependent

This paper deals with the passivity analysis problem for Takagu-Sugeno (T-S) fuzzy neural networks with mixed interval time-varying delays and uncertain parameters. The time delays comprise discrete and distributed interval time-varying delays and the uncertain parameters are norm-bounded. Delay-dependent sufficient conditions for the passivity problem are obtained by using Lyapunov-Krasovskii functional approach and linear matrix inequality (LMI) technique. The important feature of the results lies in that it does not make use of upper bounds to introduce some degree of conservativeness. Two illustrative examples are exploited in order to illustrate the effectiveness of the proposed design procedures.

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

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
Sign in / Sign up

Export Citation Format

Share Document