Recent Advances in Fuzzy System Modeling

2014 ◽  
pp. 51-66
Author(s):  
I. Burhan Türkşen
Keyword(s):  
Fuzzy System ◽  
System Modeling ◽  
Recent Advances

A neuro-fuzzy system modeling with self-constructing rule generation and hybrid SVD-based learning

2003 ◽  
Vol 11 (3) ◽  
pp. 341-353 ◽  
Author(s):  
Shie-Jue Lee ◽  
Chen-Sen Ouyang
Keyword(s):  
Fuzzy System ◽  
System Modeling ◽  
Rule Generation ◽  
Neuro Fuzzy

A Novel Takagi–Sugeno Fuzzy System Modeling Method with Joint Feature Selection and Rule Reduction

2018 ◽  
Author(s):  
Defu Lin ◽  
Jun Wang ◽  
Jihua Zhu ◽  
Yifan Wang ◽  
Yizhang Jiang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Fuzzy System ◽  
System Modeling ◽  
Modeling Method ◽  
Takagi Sugeno

Fuzzy system modeling of dynamic compaction in granular soils

2006 ◽  
Author(s):  
M Teshneh Lab ◽  
M Ghazavi ◽  
S Sadrnejad ◽  
M Pasdarpour
Keyword(s):  
Fuzzy System ◽  
System Modeling ◽  
Dynamic Compaction ◽  
Granular Soils

scholarly journals Fuzzy System Modeling Using New Hierarchical Structure

2002 ◽  
Vol 12 (5) ◽  
pp. 405-410
Author(s):  
Do-Wan Kim ◽  
Young-Hoon Joo ◽  
Jin-Bae Park
Keyword(s):  
Hierarchical Structure ◽  
Fuzzy System ◽  
System Modeling

Neuro-Fuzzy System Modeling

2010 ◽  
pp. 147-175
Author(s):  
Chen-Sen Ouyang
Keyword(s):  
Neural Networks ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
System Modeling ◽  
Fuzzy Modeling ◽  
Structure Identification ◽  
Learning Capability ◽  
Computing Paradigm ◽  
Emerging Trends ◽  
Neuro Fuzzy

Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known modeling approaches of neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability. Up to now, many approaches have been proposed for neuro-fuzzy modeling. However, it still exists many problems need to be solved. In this chapter, the authors firstly give an introduction to neuro-fuzzy system modeling. Secondly, some basic concepts of neural networks, fuzzy systems, and neuro-fuzzy systems are introduced. Also, they review and discuss some important literatures about neuro-fuzzy modeling. Thirdly, the issue for solving two most important problems of neuro-fuzzy modeling is considered, i.e., structure identification and parameter identification. Therefore, the authors present two approaches to solve these two problems, respectively. Fourthly, the future and emerging trends of neuro-fuzzy modeling is discussed. Besides, the possible research issues about neuro-fuzzy modeling are suggested. Finally, the authors give a conclusion.

scholarly journals State feedback fuzzy-model-based control for Markovian jump nonlinear systems

2004 ◽  
Vol 15 (3) ◽  
pp. 279-290 ◽  
Author(s):  
Natache S. D. Arrifano ◽  
Vilma A. Oliveira
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy System ◽  
System Modeling ◽  
Fuzzy Model ◽  
Control Design ◽  
Linear Matrix ◽  
Markovian Jump ◽  
Stabilizing Controller ◽  
Model Based Control ◽  
Model Based

This paper deals with the fuzzy-model-based control design for a class of Markovian jump nonlinear systems. A fuzzy system modeling is proposed to represent the dynamics of this class of systems. The structure of the fuzzy system is composed of two levels, a crisp level which describes the Markovian jumps and a fuzzy level which describes the system nonlinearities. A sufficient condition on the existence of a stochastically stabilizing controller using a Lyapunov function approach is presented. The fuzzy-model-based control design is formulated in terms of a set of linear matrix inequalities. Simulation results for a single-machine infinite-bus power system which is modeled as a Markovian jump nonlinear system in the infinite-bus voltage are presented to illustrate the applicability of the technique.

Handling uncertainty in eHealth sensors using fuzzy system modeling

2020 ◽  
Vol 10 (6) ◽  
pp. 1533-1554
Author(s):  
Atrayee Gupta ◽  
Nandini Mukherjee
Keyword(s):  
Fuzzy System ◽  
System Modeling

scholarly journals A Constructive-Fuzzy System Modeling for Time Series Forecasting

2007 ◽  
Author(s):  
Ivette Luna ◽  
Secundino Soares ◽  
Rosangela Ballini
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
System Modeling ◽  
Time Series Forecasting
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