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Wyszukujesz frazę "Takagi Sugeno" wg kryterium: Temat


Wyświetlanie 1-5 z 5
Tytuł:
Nonlinear actuator fault estimation observer: an inverse system approach via a T-S fuzzy model
Autorzy:
Xu, D.
Jiang, B.
Shi, P.
Powiązania:
https://bibliotekanauki.pl/articles/331464.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
model rozmyty Takagi-Sugeno
obserwator ślizgowy
system odwrotny
actuator fault estimation
Takagi-Sugeno fuzzy models
robust sliding mode observer
inverse system method
Opis:
Based on a Takagi-Sugeno (T-S) fuzzy model and an inverse system method, this paper deals with the problem of actuator fault estimation for a class of nonlinear dynamic systems. Two different estimation strategies are developed. Firstly, T-S fuzzy models are used to describe nonlinear dynamic systems with an actuator fault. Then, a robust sliding mode observer is designed based on a T-S fuzzy model, and an inverse system method is used to estimate the actuator fault. Next, the second fault estimation strategy is developed. Compared with some existing techniques, such as adaptive and sliding mode methods, the one presented in this paper is easier to be implemented in practice. Finally, two numerical examples are given to demonstrate the efficiency of the proposed techniques.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 1; 183-196
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization
Autorzy:
Soltani, M.
Chaari, A.
Ben Hmida, F.
Powiązania:
https://bibliotekanauki.pl/articles/330134.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
model rozmyty Takagi-Sugeno
algorytm grupowania
metoda najmniejszych kwadratów
optymalizacja rojem cząstek
Takagi-Sugeno fuzzy models
noise clustering algorithm
fuzzy c-regression model
orthogonal least squares
particle swarm optimization (PSO)
Opis:
This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 617-628
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparision of Two Construction Algorithms for Takagi-Sugeno Fuzzy Models
Autorzy:
Nelles, O.
Fink, A.
Babuska, R.
Setnes, M.
Powiązania:
https://bibliotekanauki.pl/articles/911156.pdf
Data publikacji:
2000
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
modelowanie
identyfikacja
model rozmyty
turbosprężarka doładowująca
modeling
identification
Takagi-Sugeno fuzzy models
local linear models
turbocharger
Opis:
This paper compares two different approaches to the construction of Takagi-Sugeno fuzzy models from data. These models approximate nonlinear systems by means of interpolation between local linear models. The main issue in the construction of Takagi-Sugeno models is the decomposition of the operating space into validity regions for the local models. The way this decomposition is done influences the complexity, accuracy and transparency of the obtained model. The first of the presented methods, the local linear model tree (LOLIMOT) algorithm generates incrementally the fuzzy model by axis-orthogonal decomposition of the input space. In the other method, product-space fuzzy clustering (the Gustafson-Kessel algorithm) is used to partition the available data into fuzzy subsets. The fundamental advantages and drawbacks of both the alternative strategies are pointed out. Their properties and real-world applicability are illustrated by building a dynamic model of a truck Diesel engine turbocharger.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2000, 10, 4; 835-855
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On choosing the fuzziness parameter for identifying TS models with multidimensional membership functions
Autorzy:
Kroll, A.
Powiązania:
https://bibliotekanauki.pl/articles/91761.pdf
Data publikacji:
2011
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
fuzzy clustering
structure/fuzzy partitioning
Takagi-Sugeno fuzzy models
TS fuzzy models
fuzziness parameter m
fuzzy classifier
Opis:
Fuzzy clustering is a well-established method for identifying the structure/fuzzy partitioning of Takagi-Sugeno (TS) fuzzy models. The clustering algorithms require choosing the fuzziness parameter m. Prior work in the area of pattern recognition shows, that a suitable choice of m is application- dependent. Yet, the default of m=2 is commonly chosen. This paper examines the suitable choice of m for identifying TS models. The focus is on models that use the classifiers resulting from fuzzy clustering as multi-dimensional membership functions or their projection and approximation. At first, the differentiability and grouping properties of the fuzzy classifiers are analyzed to make a general recommendation of choosing m(1;3). Besides, the effect of the cluster number c on the classification fuzziness is examined. Finally, requirements that are specific to TS modeling are introduced, which narrow down the suitable range for m. Building on algorithm analysis and four case studies (function approximation, a vehicle engine and an axial compressor application for nonlinear regression), it is demonstrated that choosing m2(1;1.3) for local and m2(1;1.5) for global estimation will typically provide for good results.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 4; 283-300
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Relaxed formulation of the design conditions for Takagi-Sugeno fuzzy virtual actuators
Autorzy:
Filasova, A.
Krokavec, D.
Liscinsky, P.
Powiązania:
https://bibliotekanauki.pl/articles/229418.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
nonlinear dynamic systems
Takagi-Sugeno fuzzy models
fault tolerant control
static output controllers
virtual actuators
linear matrix inequalities
Opis:
The H∞ norm approach to virtual actuators design, intended to Takagi-Sugeno fuzzy continuous-time systems, is presented in the paper. Using the second Ljapunov method, the design conditions are formulated in terms of linear matrix inequalities in adapted bounded real lemma structures. Related to the static output controller, and for systems under influence of single actuator faults, the design steps are revealed for a three-tank system plant.
Źródło:
Archives of Control Sciences; 2016, 26, 2; 199-221
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-5 z 5

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