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

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 improve 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.

Key words: dynamic forecast, cascade process neural networks, phase space reconstruction, model, application