Prediction on Moonlet Power System Data Based on Modified Probability Neural Network

摘要：

In this paper, an approach is proposed for time scries prediction based on Modified Probability Neural Network (MPNN).Bayesian-statistics and decision-making theories and non-parameters density function estimation using Parzen window function are applied to MPNN.The efficiency of the approach was demonstrated by a case study, an application for prediction on moonlet power system data, through comparison with other methods the linear ARMA model and the nonlinear widely used BP neural network.It was found that MPNN has the highest precision with the least time cost.Consequently,it verified and illustrated that large number of battery data can be predicted quickly and accurately using MPNN,moreover,it is valuable in the field of moonlet power system data prediction.

关键词： Modified PNN moonlet power system time series prediction battery

作者: Tao Lai-fa Luan Jia-hui Lu Chen

作者单位: System Engineering of Engineering Technology Beijing University of Aeronautics and Astronautics Beijing,China

会议类型: 国际会议

会议名称: 2009 8th International Conference on Reliability,Maintainability and Safety(第八届中国国际可靠性、维修性、安全性会议)

会议地点: 成都

会议语种:英文

页码: 864-867

在线出版日期: 2009-08-24（万方平台首次上网日期，不代表论文的发表时间）