Abstract
Wavelet neural networks have recently aroused great interest, because of their advantages compared to networks with radial basic functions because they are universal approximators. In this paper, we propose a robust wavelet neural network based on the Least Trimmed Square (LTS) method and Multi Library Wavelet Function (MLWF). We use a novel Beta wavelet neural network BWNN. A constructive neural network learning algorithm is used to add and train these additional neurons. The general goal of this algorithm is to minimize the number of neurons in the network during the learning phase. This phase is empowered by the use of Multi Library Wavelet Function (MLWF). The Least Trimmed Square (LTS) method is applied for selecting the wavelet candidates from the MLWF to construct the BWNN. A numerical experiment is given to validate the application of this wavelet neural network in multivariable functional approximation. The experimental results show that the proposed approach is very effective and accurate.