Guo, Y., Guo, L. Z., Billings, S. A. et al. (1 more author) (2015) An iterative orthogonal forward regression algorithm. International Journal of Systems Science, 46 (5). pp. 776-789. ISSN 0020-7721
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 Taylor and Francis. This is an author produced version of a paper subsequently published in International Journal of Systems Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | iterative orthogonal forward regression; orthogonal least squares; model structure detection; nonlinear system identification |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Nov 2016 11:51 |
Last Modified: | 16 Nov 2016 11:52 |
Published Version: | http://dx.doi.org/10.1080/00207721.2014.981237 |
Status: | Published |
Publisher: | Taylor & Francis: STM, Behavioural Science and Public Health Titles |
Refereed: | Yes |
Identification Number: | 10.1080/00207721.2014.981237 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:107315 |