Fuzzy Modeling and Control

Piegat, Andrzej

doi:10.1007/978-3-7908-1824-6

Cited by 395 publications

(85 citation statements)

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“…However, it depends on the application which variant of consequents is preferred. For instance, in control or identification problems it is often interesting to know in which parts the model behaves almost constant or which influence the different variables have in different regions [21] -this can be directly read from the (normalized) linear parameter values (close to 0 or not) which can be interpreted as variable importance weights in the corresponding regions, achieving an embedded local feature weighting and selection approach -as e.g. in [22].…”

Section: Model Architecturementioning

confidence: 99%

Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems

Lughofer

Macián

Guardiola

et al. 2011

Applied Soft Computing

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ElsevierLughofer, E.; Macian Martinez, V.; Guardiola García, C.; Klement, EP. (2011) AbstractAntipollution legislation in automotive internal combustion engines requires active control of pollutant formation and emissions. In addition to new technologies, like selective catalyst systems or diesel particulate filters, predictive emission models are needed. These models are of great use in the system calibration phase, and also can be integrated for the engine control and on-board diagnosis tasks. In this paper, fuzzy modelling of the NOx emissions of a diesel engine is investigated, which overcomes some drawbacks of pure engine mapping or analytical physical-oriented models. For building up the fuzzy NOx prediction models, the FLEXFIS approach (short for FLEXible Fuzzy Inference Systems) is applied, which automatically extracts an appropriate number of rules and fuzzy sets by an evolving version of vector quantization (eVQ) and estimates the consequent parameters of Takagi-Sugeno fuzzy systems with the local learning approach in order to optimize the least squares functional. The predictive power of the fuzzy NOx prediction models is compared with that one achieved by physical-oriented models based on high-dimensional engine data recorded during steady-state and dynamic engine states.

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Section: Model Architecturementioning

confidence: 99%

Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems

Lughofer

Macián

Guardiola

et al. 2011

Applied Soft Computing

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“…However, an excessive increase in the number of fuzzy rules may result in a non-parsimonious model that may be difficult to calibrate and risks overfitting the calibration data (Piegat 2001). Thus, there is a need to strike a balance between model complexity and model performance.…”

Section: Types Of Fismentioning

confidence: 99%

“…In addition to these, Mamdani-type fuzzy models are more suitable for representing expert knowledge that is given in the form of vague descriptions of the real system's behaviour (Cordon et al 2001;Piegat 2001 More recently, FIS have also begun to be employed in the context of river flow forecasting.…”

Section: Applicability Of Mamdani Versus Tsk-type Fismentioning

confidence: 99%

Review of the application of fuzzy inference systems in river flow forecasting

Jacquin

Shamseldin

2009

Journal of Hydroinformatics

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This paper provides a general overview about the use of fuzzy inference systems in the important field of river flow forecasting. It discusses the overall operation of the main two types of fuzzy inference systems, namely Mamdani and Takagi-Sugeno-Kang fuzzy inference systems, and the critical issues related to their application. A literature review of existing studies dealing with the use of fuzzy inference systems in river flow forecasting models is presented, followed by some recommendations for future research areas. This review shows that fuzzy inference systems can be used as effective tools for river flow forecasting, even though their application is rather limited in comparison to the popularity of neural networks models. In addition to this, it was found that there are several unresolved issues requiring further attention before more clear guidelines for the application of fuzzy inference systems can be given.

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“…A thorough description of the idea of fuzzy logic and fuzzy modelling can be found in the abundant literature, e.g., in (Driankov et al, 1993;Piegat, 2001;Yager and Filev, 1994). Remark 1.…”

Section: Takagi-sugeno Fuzzy Modelingmentioning

confidence: 99%

Effective Dual-Mode Fuzzy DMC Algorithms with On-Line Quadratic Optimization and Guaranteed Stability

Marusak¹,

Tatjewski²

2009

International Journal of Applied Mathematics and Computer Science

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Dual-mode fuzzy dynamic matrix control (fuzzy DMC-FDMC) algorithms with guaranteed nominal stability for constrained nonlinear plants are presented. The algorithms join the advantages of fuzzy Takagi-Sugeno modeling and the predictive dual-mode approach in a computationally efficient version. Thus, they can bring an improvement in control quality compared with predictive controllers based on linear models and, at the same time, control performance similar to that obtained using more demanding algorithms with nonlinear optimization. Numerical effectiveness is obtained by using a successive linearization approach resulting in a quadratic programming problem solved on-line at each sampling instant. It is a computationally robust and fast optimization problem, which is important for on-line applications. Stability is achieved by appropriate introduction of dual-mode type stabilization mechanisms, which are simple and easy to implement. The effectiveness of the proposed approach is tested on a control system of a nonlinear plant-a distillation column with basic feedback controllers.

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Fuzzy Modeling and Control

Abstract: bin/search_book.pl?series = 2941 Further volumes of this series can be found at our homepage.

Cited by 395 publications

References 0 publications

Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems

Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems

Review of the application of fuzzy inference systems in river flow forecasting

Effective Dual-Mode Fuzzy DMC Algorithms with On-Line Quadratic Optimization and Guaranteed Stability

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