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

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<mark>Journal publication date</mark>1/07/2015
<mark>Journal</mark>IFAC-PapersOnLine
Issue number10
Volume28
Number of pages6
Pages (from-to)129-134
Publication statusPublished
Original languageEnglish
Event2nd IFAC Conference on Embedded Systems, Computer Intelligence and Telematics, CESCIT 2015 - Maribor, Slovenia
Duration: 22/06/201524/06/2015

Conference

Conference2nd IFAC Conference on Embedded Systems, Computer Intelligence and Telematics, CESCIT 2015
CountrySlovenia
CityMaribor
Period22/06/1524/06/15

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.