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An elastic net orthogonal forward regression algorithm

Hong, X. and Chen, S. (2012) An elastic net orthogonal forward regression algorithm. In: 16th IFAC Symposium on System Identification, 11-13 July 2012, Brussels, Belgium, pp. 1814-1819.

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Official URL: 10.3182/20120711-3-BE-2027.0015...


In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:28185
Uncontrolled Keywords:Basis Functions; Identification for Control; Multivariable System Identification

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