Strong Regression Wavelet Neural Network Based on the Dynamic Basis Functions

In view of the wavelet neural network (WNN) that is difficult to select the appropriate wavelet functions and determine the hidden layer nodes and other issues, a method of using ensemble learning with WNN was put forward to improve the fault-tolerant ability and self-learning ability. First, the method performed the sample data using the dimensionality reduction and normalization method, and determined the distribution weights of test data. Second, it randomly selected different wavelet basis functions to construct heterogeneous predictors of WNN and repeatedly trained the sample data. Finally, AdaBoost algorithm ensemble learning is used to form a new strong predictor. A simulation verification for the database of UCI was carried out.Resultsshow that this method reduceds the average error value by more than 30% compared with the traditional wavelet neural network, and improves the forecasting accuracy and generalization ability of WNN.