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Multi-model recursive identification for nonlinear systems with non-uniformly sampling
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DOI：10.1007/s10586-016-0688-0.png)
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Abstract
En 中文
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.
Keywords:
Non-uniformly sampled-data
Nonlinear systems identification
Fuzzy c-mean cluster
Multi-model method
Recursive least squares
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