计算机工程与应用2011，Vol.47Issue(11)：145-148,4.DOI:10.3778/j.issn.1002-8331.2011.11.041
一种级联过程神经网络动态预测模型及其应用
Caseade PNN dynamic forecast model and its application.
摘要
Abstract
Aiming at the problem of nonlinear time-varying system dynamic indicators forecast in different stages,a dynamic forecast model and algorithm based on a cascade process neural network and phase space reconstruction technology are proposed in this paper. Taking into account the actual system variables have different roles and information transformation mechanism as well as the succession of system state in different stage,a cascade process neural network with some sub-networks is presented to establish the dynamic forecast model of actual system. At the same time,the theory of phase space reconstruction is applied to construct sample set so as to make up for the lack of training sample data and inprove the utilization of actual sample data. The information processing mechanisms and learning algorithm of prediction model are also proposed in this paper. Taking tertiary oil recovery process of oil field development for example,the experimental result shows the effectiveness of the proposed model and method.关键词
动态预测/级联过程神经元网络/相空间重构/模型/应用Key words
dynamic forecast/cascade process neural networks/phase space reconstruction/model/application分类
信息技术与安全科学引用本文复制引用
吴淑玲,许少华,张强..一种级联过程神经网络动态预测模型及其应用[J].计算机工程与应用,2011,47(11):145-148,4.基金项目
黑龙江省教育厅科技项目(No.11521013) （No.11521013）
黑龙江省自然科学基金(No.ZA2006-11) （No.ZA2006-11）
黑龙江省科技攻关项目(No.GZ07A103). （No.GZ07A103）
