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