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Cluster Computing

Erschienen in:

29.11.2016

Multi-model recursive identification for nonlinear systems with non-uniformly sampling

verfasst von: Ranran Liu, Tianhong Pan, Zhengming Li

Erschienen in: Cluster Computing | Ausgabe 1/2017

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Abstract

A recursive least squares based on Multi-model is proposed for non-uniformly sampled-data nonlinear (NUSDN) systems. The corresponding state space model of an NUSDN system is derived using lifting technique. Taking advantage of the Fuzzy c-Mean Clustering algorithm, NUSDN is divided into several local models. The basic idea is that the NUSDN system is viewed as a model switching system under a given rule. Once the local models are identified, the global model is determined. A pH neutralization process validate the performance of the proposed algorithm.

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Titel: Multi-model recursive identification for nonlinear systems with non-uniformly sampling
verfasst von: Ranran Liu
Tianhong Pan
Zhengming Li
Publikationsdatum: 29.11.2016
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 1/2017
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-016-0688-0

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