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.
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.