The hidden neurons selection of the wavelet networks using support vector machines and ridge regression

A 1-norm support vector machine stepwise (SVMS) algorithm is proposed for the hidden neurons selection of wavelet networks (WNs). In this new algorithm, the linear programming support vector machine (LPSVM) is employed to pre-select the hidden neurons, and then a stepwise selection algorithm based on ridge regression is introduced to select hidden neurons from the pre-selection. The main advantages of the new algorithm are that it can get rid of the influence of the ill conditioning of the matrix and deal with the problems that involve a great number of candidate neurons or a large size of samples. Four examples are provided to illustrate the efficiency of the new algorithm.