Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data

Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this paper, in order to improve the predictive capability of WNNs, the types of activation functions used in the hidden layer of the WNN were varied. The modified WNNs were then applied in approximating a benchmark piecewise function. Subsequently, performance comparisons with other developed methods in studying the same benchmark function were made. An assessment analysis showed that this proposed approach outperformed the rest. The efficiency of the modified WNNs was explored through a real-world application problem-specifically, the prediction of time-series pollution data at Texas of United States. The comparative experimental results showed that integrating different wavelet families into the hidden layer of WNNs leads to superior performance.