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Normalised Spline Adaptive Filtering Algorithm for Nonlinear System Identification

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Abstract

This paper proposed a normalised spline adaptive filtering algorithm to improve the stability of spline adaptive filtering (SAF) algorithm against the eigenvalue spread of the autocorrelation matrix of the input signal. The new adaptive filtering algorithm is based on the normalised least mean square (NLMS) approach and the value range of the learning rate in this algorithm is specified. This algorithm is called SAF-NLMS. In this work, first the derivation of the SAF-NLMS algorithm is given. Second, detailed convergence and the computational complexity analyses are carried out. Finally, the performance of the proposed algorithm is tested according to artificial datasets and real datasets. The achieved results present actually good performance. So, in practical engineering, the algorithm can be used to solve the problem of modeling or identification of nonlinear systems.

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Authors and Affiliations

  1. School of Electro-Mechanical Engineering, Xidian University, No. 2 South Taibai Road, Xi’an, 710071, Shaanxi, China

    Sihai Guan & Zhi Li

Authors
  1. Sihai Guan
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  2. Zhi Li
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Corresponding author

Correspondence to Zhi Li.

Additional information

This work was partially supported by the National Natural Science Foundation of China (Grant: 61074120, 61673310) and the State Key Laboratory of Intelligent Control and Decision of Complex Systems of Beijing Institute of Technology.

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Guan, S., Li, Z. Normalised Spline Adaptive Filtering Algorithm for Nonlinear System Identification. Neural Process Lett 46, 595–607 (2017). https://doi.org/10.1007/s11063-017-9606-6

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  • DOI: https://doi.org/10.1007/s11063-017-9606-6

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