Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
Atomic Decomposition by Basis Pursuit
SIAM Review
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Nonlinear model structure detection using optimum experimental design and orthogonal least squares
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
International Journal of Systems Science
Model selection approaches for non-linear system identification: a review
International Journal of Systems Science
IEEE Transactions on Circuits and Systems Part I: Regular Papers
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Three hybrid data based model construction/pruning formula are introduced by using backward elimination as automatic postprocessing approaches to improved model sparsity. Each of these approaches is based on a composite cost function between the model fit and one of three terms of A-/D-optimality / (parameter 1-norm in basis pursuit) that determines a pruning process. The A-/D-optimality based pruning formula contain some orthogonalisation between the pruned model and the deleted regressor. The basis pursuit cost function is derived as a simple formula without need for an orthogonalisation process. These different approaches to parsimonious data based modelling are applied to the same numerical examples in parallel to demonstrate their computational effectiveness.