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Nonlinear system identification using least squares support vector machine tuned by an adaptive particle swarm optimization

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Abstract

In this paper, we present a method for nonlinear system identification. The proposed method adopts least squares support vector machine (LSSVM) to approximate a nonlinear autoregressive model with eXogeneous (NARX). First, the orders of NARX model are determined from input–output data via Lipschitz quotient criterion. Then, an LSSVM model is used to approximate the NARX model. To obtain an efficient LSSVM model, a novel particle swarm optimization with adaptive inertia weight is proposed to tune the hyper-parameters of LSSVM. Two experimental results are given to illustrate the effectiveness of the proposed method.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (No. 61273260), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20121333120010), China Postdoctoral Science Foundation (No. 2013M530888, 2014T70229), Natural Science Foundation of Hebei Province (No. F2014203208).

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Authors and Affiliations

  1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Shuen Wang,  Zhenzhen Han, Fucai Liu & Yinggan Tang

  2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Qinhuangdao, 066004, Hebei, China

    Shuen Wang,  Zhenzhen Han, Fucai Liu & Yinggan Tang

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  1. Shuen Wang
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  2. Zhenzhen Han
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Correspondence to Yinggan Tang.

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Wang, S., Han, ., Liu, F. et al. Nonlinear system identification using least squares support vector machine tuned by an adaptive particle swarm optimization. Int. J. Mach. Learn. & Cyber. 6, 981–992 (2015). https://doi.org/10.1007/s13042-015-0403-0

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