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An iterative orthogonal forward regression algorithm

Author

Listed:
  • Yuzhu Guo
  • L.Z. Guo
  • S.A. Billings
  • Hua-Liang Wei

Abstract

A novel iterative learning algorithm is proposed to improve the classic Orthogonal Forward Regression (OFR) algorithm in an attempt to produce an optimal solution under a purely OFR framework without using any other auxiliary algorithms. The new algorithm searches for the optimal solution on a global solution space while maintaining the advantage of simplicity and computational efficiency. Both a theoretical analysis and simulations demonstrate the validity of the new algorithm.

Suggested Citation

  • Yuzhu Guo & L.Z. Guo & S.A. Billings & Hua-Liang Wei, 2015. "An iterative orthogonal forward regression algorithm," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(5), pages 776-789, April.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:5:p:776-789
    DOI: 10.1080/00207721.2014.981237
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    Cited by:

    1. Yuzhu Guo & Ling Zhong Guo & Stephen A. Billings & Hua-Liang Wei, 2016. "Identification of continuous-time models for nonlinear dynamic systems from discrete data," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(12), pages 3044-3054, September.

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