Optimal design of hierarchical wavelet networks for time-series forecasting

The purpose of this study is to identify the Hierarchical Wavelet Neural Networks (HWNN) and select important input features for each sub-wavelet neural network automatically. Based on the predefined instruction/operator sets, a HWNN is created and evolved using tree-structure based Extended Compact Genetic Programming (ECGP), and the parameters are optimized by Differential Evolution (DE) algorithm. This framework also allows input variables selection. Empirical results on benchmark time-series approximation problems indicate that the proposed method is effective and efficient.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Yuehui Chen, Bo Yang, Ajith Abraham
• Keywords: ESANN 2006, ESANN 2007, Neural Network, Sub-wavelet Neural Network, Wavelet Neural Networks