Abstract:
A clustering-based method to identify models that are piecewise affine or of Takagi-Sugeno type is presented. As prototype-based clustering algorithms, which are well sui...Show MoreMetadata
Abstract:
A clustering-based method to identify models that are piecewise affine or of Takagi-Sugeno type is presented. As prototype-based clustering algorithms, which are well suited for partitioning, frequently converge to unwanted local solutions, density-based noise clustering is used to initialize them. The clustering acts in a mixed parameter-position feature space and divides the data into separate sets for identifying local models and partition boundaries, which are assumed to be piecewise planar. The obtained partitions are tested on linearity and otherwise replaced each by a TS model that is identified from the respective data. The method is demonstrated for a test problem that includes switching, local polynomial nonlinearity as well as non-convex partition boundaries.
Published in: 2014 European Control Conference (ECC)
Date of Conference: 24-27 June 2014
Date Added to IEEE Xplore: 24 July 2014
Print ISBN:978-3-9524269-1-3