也有其不足之处,如建模过程需要建模者反复调试[13]、容易产生过拟合[14]等。在各种软计算方法中,自组织数据挖掘(self-organize data mining,SODM)是一种启发式自动建模技术[15],于1960年由Ivakhnenko提出,是多变量分析的复杂系统建模
基于20个网页-相关网页
研究结果表明,自组织数据挖掘方法将为经济预警提供一种新的模型预警法。
The results show that the self-organization data mining provides a new method to the model economic pre-warning.
简要回顾了各种电力需求预测方法,并针对最近兴起的神经网络方法的不足,提出了用自组织数据挖掘技术进行电力预测。
With a brief review to the prediction methods for electricity demand, the self-organization of data digging is introduced to predict the demand of electricity.
第三章在算法层次上提出了基于自组织竞争神经网络数据挖掘算法的知识发现系统用于生成拟定的结构方案。
Chapter 3 Applies the KDD system whose data-mining algorithm is based on Self-Organizing Neural Network for generating initial alternative designs.
应用推荐