| 龚小龙,孔玲爽,袁川来,肖会芹.非线性时间序列粒子群优化B样条网络预测模型[J].电子测量与仪器学报,2017,31(12):1890-1895 |
| 非线性时间序列粒子群优化B样条网络预测模型 |
| Nonlinear time series prediction model based on particle swarm optimization B-spline network |
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| DOI：10.13382/j.jemi.2017.12.002 |
| 中文关键词: B样条网络 粒子群算法 非线性时间序列 预测模型 |
| 英文关键词:B-spline networks particle swarm algorithm nonlinear time series prediction model |
| 基金项目:国家自然科学基金（61203136）、湖南省自然科学基金（2015JJ5025）资助项目 |
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| Author | Institution |
| Gong Xiaolong | School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China |
| Kong Lingshuang | School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China |
| Yuan Chuanlai | School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China |
| Xiao Huiqin | School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China |
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| 摘要点击次数: 2446 |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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. |
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