Iterative Evaluation of Anytime PSGS Fuzzy Systems

Takács, Orsolya; Várkonyi-Kóczy, Annamária R.

doi:10.1142/9789812562531_0006

Cited by 5 publications

(3 citation statements)

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“…(In this step only the redundancy is removed.) The SVD based model can be used in anytime systems either by applying the iterative transformation algorithm described in [7] or in the more general frame of the modular architecture presented in [8].…”

Section: Complexity Reduction Of Svd Based Modelsmentioning

confidence: 99%

Anytime Fuzzy Modeling

Várkonyi-Kóczy

2011

AMR

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Nowadays practical solutions of engineering problems involve model-integrated computing. Due to their flexibility, robustness, and easy interpretability, the application of fuzzy models, may have an exceptional role. Despite of their advantages, the usage is still limited by their exponentially increasing computational complexity. Although, combining fuzzy and anytime techniques it becomes possible to cope with the available, usually imperfect or even missing information, the dynamically changing, possibly insufficient amount of resources and reaction time. In this paper, possibilities offered by (Higher Order) Singular Value Decomposition ((HO)SVD) based anytime fuzzy models are analyzed. A modeling methodology is suggested, which offers a way for both complexity reduction and improvement of the accuracy without complexity explosion thus coping with the temporarily available amount of information and (finite) time/resources and finding the balance between accuracy and complexity.

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Section: Complexity Reduction Of Svd Based Modelsmentioning

confidence: 99%

Anytime Fuzzy Modeling

Várkonyi-Kóczy

2011

AMR

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“…In the second step, the redundancy of this model is reduced by (HO)SVD. The (non-exact) anytime models can be obtained either by applying the iterative transformation algorithm described in Takács et Várkonyi-Kóczy, 2004 or in the general frame of modular architecture (for details, see Várkonyi-Kóczy et al, 2001). In the first case, the transformation can be performed off-line and the model evaluation can be executed till the control action/results are needed.…”

Section: Reducing the Complexity Of The Modelmentioning

confidence: 99%

Anytime Models in Fuzzy Control

Várkonyi-Kóczy¹,

Bencsik²,

Ruano³

2010

Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics

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In time critical applications, anytime mode of operation offers a way to ensure continuous operation and to cope with the possibly dynamically changing time and resource availability. Soft Computing, especially fuzzy model based operation proved to be very advantageous in power plant control where the high complexity, nonlinearity, and possible partial knowledge usually limit the usability of classical methods. Higher Order Singular Value Decomposition based complexity reduction makes possible to convert different classes of fuzzy models into anytime models, thus offering a way to combine the advantages, like low complexity, flexibility, and robustness of fuzzy and anytime techniques. By this, a model based anytime control methodology can be suggested which is able to keep on continuous operation using nonexact, approximate models of the plant, thus preventing critical breakdowns in the operation. In this paper, an anytime modeling method is suggested which makes possible to use complexity optimized fuzzy models in control. The technique is able to filter out the redundancy of fuzzy models and can determine the near optimal non-exact model of the plant considering the available time and resources. It also offers a way to improve the granularity (quality) of the model by building in new information without complexity explosion.

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“…The SVD based model can be used in anytime systems either by applying the iterative transformation algorithm described in [8] or in the more general frame of the modular architecture presented in [2].…”

Section: A Reducing the Complexity Of The Modelmentioning

confidence: 99%

Anytime modeling: Compression and improvement of the approximation of SC based mappings

Várkonyi-Kóczy¹,

Várkonyi

2009

2009 4th International Symposium on Computational Intelligence and Intelligent Informatics

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Nowadays practical solutions of engineering problems involve model-integrated computing. Due to their flexibility, robustness, and easy interpretability, the application of soft computing based models, may have an exceptional role. Despite of their advantages, the usage is still limited by their exponentially increasing computational complexity. Although, combining soft computing and anytime techniques it becomes possible to cope with the available, usually imperfect or even missing information, the dynamically changing, possibly insufficient amount of resources and reaction time. In this paper, possibilities offered by (Higher Order) Singular Value Decomposition ((HO)SVD) based anytime Soft Computational (SC) models are analyzed. With the help of SVD, the redundancy of fuzzy systems and neural nets can be removed and further, non-exact reduction can also be obtained. The method also offers a way forimproving the model if later on we get into possession of new information or more resources. An algorithm is suggested, which finds the common minimal implementation space of the compressed original and the new approximation points, thus the two techniques, non-exact complexity reduction and improvement of the approximation accuracy, ensure that we can always cope with the temporarily available (finite) time/resources and find the balance between accuracy and complexity.

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Iterative Evaluation of Anytime PSGS Fuzzy Systems

Cited by 5 publications

References 0 publications

Anytime Fuzzy Modeling

Anytime Fuzzy Modeling

Anytime Models in Fuzzy Control

Anytime modeling: Compression and improvement of the approximation of SC based mappings

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