摘要
小波神经网络是近年来在小波分析研究获得突破性进展基础上提出的一种前馈型网络,文章将小波与神经网络相结合,提出了一种基于自适应小波神经网络(SAWNN,self-adaptation wavelet neural network)的数据挖掘方法,并构造了数据挖掘过程的机器学习机制,以提高对问题的处理能力。文章将所构造的自适应小波神经网络用于石油产量的建模预测研究,实证结果表明此预测模型不仅是有效的,而且是可行的。
Wavelet neural network, which is based on wavelet analysis, is sort of feed forward network developed in recent years. In this paper, combining the theories of wavelet and neural network together, a new method of the self-adaptation wavelet neural network for data mining is proposed and a machine study mechanism is then constructed in order to improve the capability of the former in tackling problems. Later on, the self-adaptation wavelet neural network is used to model and predict the petroleum yield, and the following results successfully prove that such an application is effective and feasible.
出处
《财经研究》
CSSCI
北大核心
2006年第3期114-120,共7页
Journal of Finance and Economics
关键词
石油产量
预测研究
自适应小波神经网络
petroleum yield
prediction study
self-adaptation wavelet neural network
