Fuzzy Identification of Systems and Its Applications to Modeling and Control

Takagi, Toshihisa; Sugeno, Michio

doi:10.1016/b978-1-4832-1450-4.50045-6

Cited by 968 publications

(1,197 citation statements)

References 2 publications

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“…A FIS consists of five major steps: fuzzification of input variables, application of fuzzy operators (AND, OR, and NOT) in the rule's antecedent, implication from the antecedent to the consequent, aggregation of consequent across the rules, and defuzzification (Asoodeh and Bagheripour 2012a). Defining appropriate membership functions (fuzzy sets) which best fit the data set is an important task that is done by subtractive clustering algorithm in Takagi and Sugeno (1985) FIS. Subtractive clustering is an effective approach to estimate the number of fuzzy clusters and cluster centers in Takagi-Sugeno fuzzy inference system (Jarrah and Halawani 2001).…”

Section: Fuzzy Logicmentioning

confidence: 99%

Fuzzy modeling of volume reduction of oil due to dissolved gas runoff and pressure release

Zargar

Bagheripour

Asoodeh

2014

J Petrol Explor Prod Technol

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Oil formation volume factor (FVF) refers to the change in oil volume between reservoir and standard conditions at surface. It is a crucial oil property which is governed by reservoir temperature, amount of dissolved gas in oil, and specific gravity of oil and dissolved gas. This parameter plays a trivial role in petroleum reservoir and production calculations. Accurate determination of oil FVF is restricted by limitations on reliable sampling and high cost and time-consumption associated with laboratory experiments. Furthermore, available empirical correlations do not have satisfying generalization and accuracy owing to being calibrated on specific oil samples. Therefore, this study offers a Takagi-Sugeno (TS) fuzzy logic model for estimating oil FVF for the purpose of developing a precise model calibrated on regional Iranian oil using 367 training samples. TS fuzzy model utilizes subtractive clustering approach for determining number of rules and clusters. Evaluation of constructed fuzzy logic using 108 unseen test data points indicated achievement of fuzzy logic in prediction of oil FVF.

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Section: Fuzzy Logicmentioning

confidence: 99%

Fuzzy modeling of volume reduction of oil due to dissolved gas runoff and pressure release

Zargar

Bagheripour

Asoodeh

2014

J Petrol Explor Prod Technol

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“…[25,26]. Once the optimal consequent parameters have been found, the backward pass starts immediately.…”

Section: Hybrid Learning Algorithm Of Anfismentioning

confidence: 99%

3D Image Analysis and Artificial Intelligence for Bone Disease Classification

et al. 2009

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International audienceIn order to prevent bone fractures due to disease and ageing of the population, and to detect problems while still in their early stages, 3D bone micro architecture needs to be investigated and characterized. Here, we have developed various image processing and simulation techniques to investigate bone micro architecture and its mechanical stiffness. We have evaluated morphological, topological and mechanical bone features using artificial intelligence methods. A clinical study is carried out on two populations of arthritic and osteoporotic bone samples. The performances of Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machines (SVM) and Genetic Algorithm (GA) in classifying the different samples have been compared. Results show that the best separation success (100 %) is achieved with Genetic Algorithm

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“…The Takagi -Sugeno (T S) fuzzy model approach has nowadays become popular since [1][2] [6], it showed its efficiency to control complex nonlinear systems with uncertainties parameters and used for many applications [7]. This fuzzy dynamic model is a piecewise interpolation of several linear models through membership functions [4].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Robust H∞ Tracking Control For Wind Generator System: LMI approach

Lahmadi

Boumhidi

2017

TMLAI

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This study concerns the tracking control problem of the wind turbine generator system with uncertainties parameters and external disturbances. Based on T S fuzzy model, a fuzzy observer based and a fuzzy robust state feedback output tracking control are developed to reduce the tracking error by minimizing the disturbance level caused by the wind speed. Using a Lyapunov function combined with H∞ tracking criteria and a judicious of the famous Young relation, a sufficient stability condition for the robust fuzzy tracking control formulated in terms of linear matrix inequality, which can be very efficiently solved by using LMI optimization techniques. The simulation results are given to show the performance of the observer based tracking controller.

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Fuzzy Identification of Systems and Its Applications to Modeling and Control

Cited by 968 publications

References 2 publications

Fuzzy modeling of volume reduction of oil due to dissolved gas runoff and pressure release

Fuzzy modeling of volume reduction of oil due to dissolved gas runoff and pressure release

3D Image Analysis and Artificial Intelligence for Bone Disease Classification

Fuzzy Robust H∞ Tracking Control For Wind Generator System: LMI approach

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