scholarly journals Takagi-Sugeno Neuro-Fuzzy Modeling of a Multivariable Nonlinear Antenna System

2005 ◽  
Vol 2 (1) ◽  
pp. 12
Author(s):  
E. A. Al-Gallaf
Keyword(s):  
Dynamic System ◽  
Fuzzy System ◽  
System Modeling ◽  
Nonlinear Dynamic System ◽  
Fuzzy Modeling ◽  
Antenna System ◽  
Approximation Techniques ◽  
Intelligent Modeling ◽  
Neuro Fuzzy ◽  
Takagi Sugeno

This article investigates the use of a clustered based neuro-fuzzy system to nonlinear dynamic system modeling. It is focused on the modeling via Takagi-Sugeno (T-S) modeling procedure and the employment of fuzzy clustering to generate suitable initial membership functions. The T-S fuzzy modeling has been applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs. Compared to other well-known approximation techniques such as artificial neural networks, the employed neuro-fuzzy system has provided a more transparent representation of the nonlinear antenna system under study, mainly due to the possible linguistic interpretation in the form of rules. Created initial memberships are then employed to construct suitable T-S models. Furthermore, the T-S fuzzy models have been validated and checked through the use of some standard model validation techniques (like the correlation functions). This intelligent modeling scheme is very useful once making complicated systems linguistically transparent in terms of the fuzzy if-then rules. 

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.

On interval-data based Type-1 Takagi-Sugeno fuzzy systems for uncertain nonlinear dynamic system identification

2016 ◽  
Vol 64 (6) ◽  
Author(s):  
Salman Zaidi ◽  
Andreas Kroll
Keyword(s):  
Fuzzy System ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic System ◽  
Random Variable ◽  
Interval Data ◽  
Fuzzy Modelling ◽  
Symbolic Data ◽  
Specific Assumption ◽  
Takagi Sugeno

AbstractA novel interval-data based Takagi-Sugeno fuzzy system is proposed to identify uncertain nonlinear dynamic systems by endowing the classical TS fuzzy system with probability theory and symbolic data analysis. Such systems have variability in their outputs, that is they produce varying responses each time when the same stimuli is applied to them under the same condition. Interval data is generated by repeating the identification experiment multiple times and applying the probabilistic techniques to get soft bounds of output. The interval data is then directly used in the TS fuzzy modelling, giving rise to interval antecedent and consequent parameters. This method does not require any specific assumption on the probability distribution of the random variable that models the uncertainty. The developed procedure is demonstrated for a pneumatic drive system.

CHECKING ORTHOGONAL TRANSFORMATIONS AND GENETIC ALGORITHMS FOR SELECTION OF FUZZY RULES BASED ON INTERPRETABILITY-ACCURACY CONCEPTS

2012 ◽  
Vol 20 (supp02) ◽  
pp. 159-186 ◽  
Author(s):  
M. ISABEL REY ◽  
MARTA GALENDE ◽  
M. J. FUENTE ◽  
GREGORIO I. SAINZ-PALMERO
Keyword(s):  
Fuzzy System ◽  
Poor Performance ◽  
Fuzzy Modeling ◽  
Nsga Ii ◽  
Trade Off ◽  
Rule Selection ◽  
Fuzzy Models ◽  
Neuro Fuzzy ◽  
Firing Strength ◽  
Selection Of

Fuzzy modeling is one of the most known and used techniques in different areas to model the behavior of systems and processes. In most cases, as in data-driven fuzzy modeling, these fuzzy models reach a high performance from the point of view of accuracy, but from other points of view, such as complexity or interpretability, they can present a poor performance. Several approaches are found in the bibliography to reduce the complexity and improve the interpretability of the fuzzy models. In this paper, a post-processing approach is carried out via rule selection, whose aim is to choose the most relevant rules for working together on the well-known accuracy-interpretability trade-off. The rule relevancy is based on Orthogonal Transformations, such as the SVD-QR rank revealing approach, the P-QR and OLS transformations. Rule selection is carried out using a genetic algorithm that takes into account the information obtained by the Orthogonal Transformations. The main objective is to check the true significance, drawbacks and advantages of the rule selection based on the orthogonal transformations via the rule firing strength matrix. In order to carry out this aim, a neuro-fuzzy system, FasArt (Fuzzy Adaptive System ART based), and several case studies, data sets from the KEEL Project Repository, are used to tune and check this selection of rules based on orthogonal transformations, genetic selection and accuracy-interpretability trade-off. This neuro-fuzzy system generates Mamdani fuzzy rule based systems (FRBSs), in an approximative way. NSGA-II is the MOEA tool used to tune the proposed rule selection.

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