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Adaptive model structures for nonlinear system identification

Dydyński A.

Systems Science

2008

Vol. 34, no 2

35-41

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.

An algorithm of incremental construction of nonlinear parametric approximators

Arabas J., Dydyński A.

Prace Naukowe Politechniki Warszawskiej. Elektronika

2006

z. 156

31-38

A novel algorithm of incremental construction of nonlinear parametric approximators is introduced that needs a relatively small number of parameters to achieve good accuracy. The approximator is defined as a linear combination of nonlinear base functions, and the method adds the base functions one by one, observing the correlation between the residue function and the candidate for the base function. The base functions do not need to be homogenous, i.e. to share the same formula. The method was experimentally verified and compared to neural networks, and the preliminary results were encouraging enough to develop the presented approach further.