Discrete Process Neural Networks and Its Application in the Predication of Sunspot Number Series
Considering that inputs of a process neural network (PNN) are generally time-varying functions while the inputs of many practical problems are discrete values of multiple series, in this paper, a process neural network with discrete inputs is presented to provide improved forecasting results for solving the complex time series prediction. The presented method first makes discrete input series carry out Walsh transformation, and submits the transformed series to the network for training. It can solve the problem of space-time aggregation operation of PNN. In order to examine the effectiveness of the presented method, the actual data of sunspots during 1749-2007 are employed. To predict the number of sunspots, the suitability of the developed model is examined in comparison with the other models to show its superiority and be an effective way of improving forecasting accuracy of networks.
Discrete process neural networks Learning algorithm Time series predication Sunspot number
Li Xin Cheng Chuntian
School of Computer and Information Technology, Daqing Petroleum Institute, Daqing, 163318, China Sch Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024,
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
4339-4342
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）