PCNN Forecasting Model Based on Wavelet Transform and Its Application
- 10.2991/iske.2007.58How to use a DOI?
- Pulse Coupled Neural Network; A Trous Wavelet Transform; Forecasting; Correlation Analysis; Precipitation
Pulse Coupled Neural Network (PCNN), called the third generation of neural network, is widely used in image processing for its basic characteristics of coupling mechanism and achieved some results. PCNN is improved based on the above basic characteristics in the paper: correlation coefficient is used to control the bonding strength and the threshold setting is adjusted by the least error; the operation mechanism of network is different from the PCNN used in the past; A Trous transform is combined with PCNN model to form the combination forecasting model. The improved combination model was implemented in annual rainfall forecasting to check its feasibility. With good results, it demonstrated the above forecasting model is feasible. This paper is concentrated on the improved PCNN model, expanding the application area of PCNN and provides a new forecasting theories and methods in hydrology and water resources.
- © 2007, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Qiang Fu AU - Yan Feng AU - Deng-chao Feng PY - 2007/10 DA - 2007/10 TI - PCNN Forecasting Model Based on Wavelet Transform and Its Application BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 344 EP - 350 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.58 DO - 10.2991/iske.2007.58 ID - Fu2007/10 ER -