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Comparison approaches for identification of all-data cloud-based evolving systems

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Comparison approaches for identification of all-data cloud-based evolving systems. / Blazic, Saso; Angelov, Plamen; Skrjanc, Igor.
IFAC ESCIT. IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control ESCIT, 2015.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Blazic, S, Angelov, P & Skrjanc, I 2015, Comparison approaches for identification of all-data cloud-based evolving systems. in IFAC ESCIT. IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control ESCIT.

APA

Blazic, S., Angelov, P., & Skrjanc, I. (2015). Comparison approaches for identification of all-data cloud-based evolving systems. In IFAC ESCIT IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control ESCIT.

Vancouver

Blazic S, Angelov P, Skrjanc I. Comparison approaches for identification of all-data cloud-based evolving systems. In IFAC ESCIT. IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control ESCIT. 2015

Author

Blazic, Saso ; Angelov, Plamen ; Skrjanc, Igor. / Comparison approaches for identification of all-data cloud-based evolving systems. IFAC ESCIT. IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control ESCIT, 2015.

Bibtex

@inproceedings{94c4c4647c55451eb5e42ca68cb80124,
title = "Comparison approaches for identification of all-data cloud-based evolving systems",
abstract = "In this paper we deal with identification of nonlinear systems which are modelled by fuzzy rule-based models that do not assume fixed partitioning of the space of antecedent variables. We first present an alternative way of describing local density in the cloud-based evolving systems. The Mahalanobis distance among the data samples is used which leads to the density that is more suitable when the data are scattered around the input-output surface. All the algorithms for the identification of the cloud parameters are given in a recursive form which is necessary for the implementation of an evolving system. It is also shown that a simple linearised model can be obtained without identification of the consequent parameters. All the proposed algorithms are illustrated on a simple simulation model of a static system.",
keywords = "Search methods and decision-making: neural networks, evolutionary computing, fuzzy techniques, Training and adaptation algorithms, Constructive algorithms",
author = "Saso Blazic and Plamen Angelov and Igor Skrjanc",
year = "2015",
month = jun,
day = "22",
language = "English",
booktitle = "IFAC ESCIT",
publisher = "IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control ESCIT",

}

RIS

TY - GEN

T1 - Comparison approaches for identification of all-data cloud-based evolving systems

AU - Blazic, Saso

AU - Angelov, Plamen

AU - Skrjanc, Igor

PY - 2015/6/22

Y1 - 2015/6/22

N2 - In this paper we deal with identification of nonlinear systems which are modelled by fuzzy rule-based models that do not assume fixed partitioning of the space of antecedent variables. We first present an alternative way of describing local density in the cloud-based evolving systems. The Mahalanobis distance among the data samples is used which leads to the density that is more suitable when the data are scattered around the input-output surface. All the algorithms for the identification of the cloud parameters are given in a recursive form which is necessary for the implementation of an evolving system. It is also shown that a simple linearised model can be obtained without identification of the consequent parameters. All the proposed algorithms are illustrated on a simple simulation model of a static system.

AB - In this paper we deal with identification of nonlinear systems which are modelled by fuzzy rule-based models that do not assume fixed partitioning of the space of antecedent variables. We first present an alternative way of describing local density in the cloud-based evolving systems. The Mahalanobis distance among the data samples is used which leads to the density that is more suitable when the data are scattered around the input-output surface. All the algorithms for the identification of the cloud parameters are given in a recursive form which is necessary for the implementation of an evolving system. It is also shown that a simple linearised model can be obtained without identification of the consequent parameters. All the proposed algorithms are illustrated on a simple simulation model of a static system.

KW - Search methods and decision-making: neural networks, evolutionary computing, fuzzy techniques

KW - Training and adaptation algorithms

KW - Constructive algorithms

M3 - Conference contribution/Paper

BT - IFAC ESCIT

PB - IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control ESCIT

ER -