摘要
为解决复杂时间序列的预测问题,针对目前过程神经网络的输入为多个连续的时变函数,而许多实际问题的输入为多个序列的离散值,提出一种基于离散输入的过程神经网络模型及学习算法;并以太阳黑子数实际数据为例对太阳黑子数时间序列进行预测,仿真结果表明该模型具有很好的逼近和预测能力。
By now,the input of Process Neural Network（PNN） is multiple continuous time-varying function and the input for practical problems is discrete value of multiple series, a PNN model and learning algorithm is presented based on discrete input to solve the problem of complex time series prediction.The algorithm takes sunspot number as example to predict sunspot number time series, and the simulation results show that the model produces good ability for approximation and prediction.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第32期224-227,共4页
Computer Engineering and Applications
基金
中国博士后科学基金资助项目(No.20090460864)
黑龙江省教育厅科学技术研究资助项目(No.11551015)
关键词
过程神经元网络
学习算法
时间序列预测
太阳黑子数
process neural networks
learning algorithm
time series predication
sunspot number
