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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Acknowledgements
The authors would like to thank the financial support provided by National Natural Science Foundation under Grant 61273142, Foundation for Six Talents by Jiangsu Province (2012-DZXX-045), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Jiangsu support of science and technology projects under Grant BY2016030-16 Jiangsu Planned Projects for Postdoctoral Research Funds (Grant No.1601138B).
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Liu, R., Pan, T. & Li, Z. Multi-model recursive identification for nonlinear systems with non-uniformly sampling. Cluster Comput 20, 25–32 (2017). https://doi.org/10.1007/s10586-016-0688-0
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DOI: https://doi.org/10.1007/s10586-016-0688-0