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

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Comparison of approaches for identification of all-data cloud-based evolving systems. / Blažič, Sašo; Angelov, Plamen; Škrjanc, Igor.
In: IFAC-PapersOnLine, Vol. 28, No. 10, 01.07.2015, p. 129-134.

Research output: Contribution to Journal/Magazine › Conference article › peer-review

Harvard

Blažič, S, Angelov, P & Škrjanc, I 2015, 'Comparison of approaches for identification of all-data cloud-based evolving systems', IFAC-PapersOnLine, vol. 28, no. 10, pp. 129-134. https://doi.org/10.1016/j.ifacol.2015.08.120

APA

Blažič, S., Angelov, P., & Škrjanc, I. (2015). Comparison of approaches for identification of all-data cloud-based evolving systems. IFAC-PapersOnLine, 28(10), 129-134. https://doi.org/10.1016/j.ifacol.2015.08.120

Vancouver

Blažič S, Angelov P, Škrjanc I. Comparison of approaches for identification of all-data cloud-based evolving systems. IFAC-PapersOnLine. 2015 Jul 1;28(10):129-134. doi: 10.1016/j.ifacol.2015.08.120

Author

Blažič, Sašo ; Angelov, Plamen ; Škrjanc, Igor. / Comparison of approaches for identification of all-data cloud-based evolving systems. In: IFAC-PapersOnLine. 2015 ; Vol. 28, No. 10. pp. 129-134.

Bibtex

@article{be9e419fc73b4c24a75b74924fd13c92,

title = "Comparison of 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 = "Clusters, Evolving systems, Identification, Mahalanobis distance, Takagi-Sugeno model",

author = "Sa{\v s}o Bla{\v z}i{\v c} and Plamen Angelov and Igor {\v S}krjanc",

year = "2015",

month = jul,

day = "1",

doi = "10.1016/j.ifacol.2015.08.120",

language = "English",

volume = "28",

pages = "129--134",

journal = "IFAC-PapersOnLine",

issn = "2405-8963",

publisher = "IFAC Secretariat",

number = "10",

note = "2nd IFAC Conference on Embedded Systems, Computer Intelligence and Telematics, CESCIT 2015 ; Conference date: 22-06-2015 Through 24-06-2015",

}

RIS

TY - JOUR

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

AU - Blažič, Sašo

AU - Angelov, Plamen

AU - Škrjanc, Igor

PY - 2015/7/1

Y1 - 2015/7/1

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 - Clusters

KW - Evolving systems

KW - Identification

KW - Mahalanobis distance

KW - Takagi-Sugeno model

U2 - 10.1016/j.ifacol.2015.08.120

DO - 10.1016/j.ifacol.2015.08.120

M3 - Conference article

AN - SCOPUS:84992499999

VL - 28

SP - 129

EP - 134

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 10

T2 - 2nd IFAC Conference on Embedded Systems, Computer Intelligence and Telematics, CESCIT 2015

Y2 - 22 June 2015 through 24 June 2015

ER -

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