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

Blažič, Sašo and Angelov, Plamen and Škrjanc, Igor (2015) Comparison of approaches for identification of all-data cloud-based evolving systems. IFAC-PapersOnLine, 28 (10). pp. 129-134. ISSN 2405-8963

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.