摘要
为了提高非线性时间序列的预测精度,建立一种粒子群优化B样条网络预测模型。在设计网络结构时,设置样条基函数节点作为独立变量,然后使其与权值参数在网络训练过程中一同优化,并且使用预测误差平方和评价训练效果。采用粒子群算法与适当的搜索策略作为训练算法,对B样条基函数最优节点的分布进行搜索,同时寻优权值参数,使网络结构得到优化,进而对非线性时间序列进行预测。仿真结果表明,粒子群优化B样条网络预测模型具有良好的泛化性能,同时所用算法对网络进行了有效的优化,所建预测模型结构简单且预测精度较高。
In order to improve the prediction accuracy of nonlinear time series, a prediction model based on particle swarm optimization B-spline network is proposed. In designing the structure of the network, the nodes of B-spline basis functions which are considered to be independent variables and every correlative weight parameter are to be optimized together in the network training process. And the forecasting error square sum is adopted to evaluate the training effect of the network. A particle swarm optimization algorithm with an appropriate search strategy is used as the training algorithm to search the distribution of optimal nodes of B-spline basis functions and find the optimal weight parameters, so that the structure of the network is optimized. Then, the nonlinear time series is predicted by the network. The simulation results indicate that the prediction model based on particle swarm optimization B-spline network has a fine generalization performance, and the algorithm optimizes the network effectively. The proposed prediction model is not only simple in structure, but also has higher prediction accuracy.
出处
《电子测量与仪器学报》
CSCD
北大核心
2017年第12期1890-1895,共6页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(61203136)
湖南省自然科学基金(2015JJ5025)资助项目
关键词
B样条网络
粒子群算法
非线性时间序列
预测模型
B-spline networks
particle swarm algorithm
nonlinear time series
prediction model
作者简介
龚小龙，1989年出生，2014年于洛阳理工学院获得学士学位，现为湖南工业大学硕士研究生，主要研究方向为复杂过程建模、控制与优化。E—mail：1018806258@qq．com;孔玲爽（通信作者），1979年出生，2005、2010年于中南大学获得硕士学位和博士学位，现为湖南工业大学教授，主要从事复杂工业过程建模与优化控制方面的研究。E—mail：konglingsh@126．com
