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

Blazic, Saso and Angelov, Plamen and Skrjanc, Igor (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.

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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.

Item Type:
Contribution in Book/Report/Proceedings
Subjects:
ID Code:
74606
Deposited By:
Deposited On:
26 Jan 2016 14:30
Refereed?:
Yes
Published?:
Published
Last Modified:
30 Oct 2020 08:50