Identification of the linear parts of nonlinear systems for fuzzy modeling

Sadrabadi, Mahmood Rezaei; Zarandi, M. H. Fazel

doi:10.1016/j.asoc.2009.12.041

Cited by 9 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Fuzzy rules can be generated through training algorithms such as the descent gradient method (Zhao et al, 2008;Aflab and Kadri, 2013), the recursive least squares algorithm (Xu and Xuesong, 2014) and the orthogonal least squares method (Soltani et al, 2011). Hybrid algorithms are also interesting training methods in order to obtain simple and accurate models (Chen and Linkens, 2004;Abdelazim and Malik, 2005;Jin, 2000;Lavygina and Hodashinsky, 2011;Chuang et al, 2001Chuang et al, , 2009Rezaei Sadrabadi and Fazel Zarandi, 2011). For example, the clustering approach and the descent gradient method were used for the estimation and the reduction of fuzzy models (Chen and Linkens, 2004).…”

Section: Introductionmentioning

confidence: 99%

Two fuzzy internal model control methods for nonlinear uncertain systems

Aydi

Djemel

Chtourou

2017

IJICC

View full text Add to dashboard Cite

Purpose The purpose of this paper is to use the internal model control to deal with nonlinear stable systems affected by parametric uncertainties. Design/methodology/approach The dynamics of a considered system are approximated by a Takagi-Sugeno fuzzy model. The parameters of the fuzzy rules premises are determined manually. However, the parameters of the fuzzy rules conclusions are updated using the descent gradient method under inequality constraints in order to ensure the stability of each local model. In fact, without making these constraints the training algorithm can procure one or several unstable local models even if the desired accuracy in the training step is achieved. The considered robust control approach is the internal model. It is synthesized based on the Takagi-Sugeno fuzzy model. Two control strategies are considered. The first one is based on the parallel distribution compensation principle. It consists in associating an internal model control for each local model. However, for the second strategy, the control law is computed based on the global Takagi-Sugeno fuzzy model. Findings According to the simulation results, the stability of all local models is obtained and the proposed fuzzy internal model control approaches ensure robustness against parametric uncertainties. Originality/value This paper introduces a method for the identification of fuzzy model parameters ensuring the stability of all local models. Using the resulting fuzzy model, two fuzzy internal model control designs are presented.

show abstract

Section: Introductionmentioning

confidence: 99%

Two fuzzy internal model control methods for nonlinear uncertain systems

Aydi

Djemel

Chtourou

2017

IJICC

View full text Add to dashboard Cite

show abstract

“…Sadrabadi and Zarandi [21] propose an algorithm to classify input-output points into two categories: the points located in the linear parts and the point located in the extrema.…”

Section: Clusteringmentioning

confidence: 99%

Facility location via fuzzy modeling and simulation

Rezaei¹,

Zarandi²

2011

Applied Soft Computing

View full text Add to dashboard Cite

This paper presents a continuous facility location model with fuzzy methodology. The developments concern mainly to some drawbacks in the initial model which takes it far from being used in practice. A fuzzy modeling method is proposed to estimate the required functions in the initial model. Structure identification in the proposed fuzzy modeling method is carried out using subtractive clustering, and parameter identification is conducted via some heuristics as well as an optimization problem.Furthermore, a simulation method along with some heuristic relations is used for implementation and evaluation of the modified model. Efficiency of the proposed method to fuzzy modeling as well as the proposed simulation method is presented by a numerical example.

show abstract

“…Подавляющее большинство методов формирования структуры основано на методах нечеткого кластерного анализа, среди которых наиболее часто применяемыми являются метод нечетких c-средних, алгоритмы Gustafson-Kessel и Gath-Geva, метод нечеткой c-регрессии (FCRM) [4], алгоритм субтрактивной кластеризации [5], а также комбинации указанных методов [6]. Среди перечисленных выше методов FCRM, формирующий форму кластера в виде гиперплоскости, наиболее предпочтителен для решения задач построения нечетких систем типа Такаги-Сугено.…”

unclassified

Technique for designing accurate and compact Takagi–Sugeno fuzzy systems

Hodashinskiy¹,

Carin²

2016

Proceedings of TUSUR

View full text Add to dashboard Cite

50УДК 004.85 И.А. Ходашинский, К.С. Сарин Методика построения компактных и точных нечетких систем типа Такаги-СугеноРассматриваются этапы построения нечетких систем типа Такаги-Сугено: формирование исходных данных, генерация структуры нечеткой системы, оптимизация параметров антецедентов и консеквентов нечетких правил. Компромисс между компактностью и точностью модели достигается использованием трех статистических информационных критериев: Akaike, Bayesian, Hannan-Quinn Information Criterion. Приведены результаты экспериментов по аппроксимации реальных данных. Ключевые слова: нечеткие системы, динамическое разбиение, кусочно-линейная инициализация, алгоритм «кукушкин поиск», рекуррентный метод наименьших квадратов, статистические информационные критерии.

show abstract

Identification of the linear parts of nonlinear systems for fuzzy modeling

Cited by 9 publications

References 22 publications

Two fuzzy internal model control methods for nonlinear uncertain systems

Two fuzzy internal model control methods for nonlinear uncertain systems

Facility location via fuzzy modeling and simulation

Technique for designing accurate and compact Takagi–Sugeno fuzzy systems

Contact Info

Product

Resources

About