Published paper Wei, H.L and Billings, S.A (2008) An adaptive orthogonal search algorithm for model subset selection and non-linear system identification. International Journal of Control, 81 (5). pp. 714-724. Abstract: A new adaptive orthogonal search (AOS) algorithm is proposed for model subset selection and nonlinear system identification. Model structure detection is a key step in any system identification problem. This consists of selecting significant model terms from a redundant

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... y of candidate model terms, and determining the model complexity (model length or model size). The final objective is to produce a parsimonious model that can well capture the inherent dynamics of the underlying system. In the new AOS algorithm, a modified generalized cross-validation criterion, called the adjustable prediction error sum of squares (APRESS), is introduced and incorporated into a forward orthogonal search procedure. The main advantage of the new AOS algorithm is that the mechanism is simple and the implementation is direct and easy, and more importantly it can produce efficient model subsets for most nonlinear identification problems. Abstract: A new adaptive orthogonal search (AOS) algorithm is proposed for model subset selection and nonlinear system identification. Model structure detection is a key step in any system identification problem. This consists of selecting significant model terms from a redundant dictionary of candidate model terms, and determining the model complexity (model length or model size). The final objective is to produce a parsimonious model that can well capture the inherent dynamics of the underlying system. In the new AOS algorithm, a modified generalized cross-validation criterion, called the adjustable prediction error sum of squares (APRESS), is introduced and incorporated into a forward orthogonal search procedure. The main advantage of the new AOS algorithm is that the mechanism is simple and the implementation is direct and easy, and more importantly it can produce efficient model subsets for most nonlinear identification problems.