Regressor selection and wavelet network construction
Résumé
The wavelet network has been introduced as a special feedforward neural network supported by the wavelet theory. Such network can be directly used in function approximation problems and consequently can be applied to nonlinear system modeling by means of nonlinear black-box identification. In this paper the construction of feedforward neural networks is discussed from both identification and regressor selection points of view. This reveals that the wavelet network structure is well suited for developing constructive methods for feedforward networks. An efficient initialization procedure of the wavelet network based on the orthogonal least squares (OLS) method is then proposed. The efficiency of the wavelet network and the proposed procedure for nonlinear system modeling is illustrated by a numerical example.