This article addresses nonlinear black-box identification using parametric models. A novel adaptive method for the choice of both the model structure and its parameter values is introduced. Experiments in nonlinear time-series modelling and prediction are presented. The method can be seen as unification model of neural networks of all types in the sense as meta-learning applies to the area of predictive data mining, to combine the predictions from multiple models.
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