# [1]李博伟,许飞云,杨会超.RBF-BL时间序列模型及其在建模和预测中的应用[J].东南大学学报(自然科学版),2020,50(2):368-376.[doi:10.3969/j.issn.1001-0505.2020.02.022] 　Li Bowei,Xu Feiyun,Yang Huichao.RBF-BL time series model and its application in modeling and prediction[J].Journal of Southeast University (Natural Science Edition),2020,50(2):368-376.[doi:10.3969/j.issn.1001-0505.2020.02.022] 点击复制 RBF-BL时间序列模型及其在建模和预测中的应用() 分享到： var jiathis_config = { data_track_clickback: true };

50

2020年第2期

368-376

2020-03-20

## 文章信息/Info

Title:
RBF-BL time series model and its application in modeling and prediction

Author(s):
School of Mechanical Engineering, Southeast University, Nanjing 211189, China

Keywords:

TP391
DOI:
10.3969/j.issn.1001-0505.2020.02.022

Abstract:
An improved nonlinear model was proposed based on the traditional linear and nonlinear models, that is radial basis function neural network based state dependent bilinear(RBF-BL)model. The sum of squares of the model residuals was taken as the objective function and the parameter estimation algorithm was introduced. The sunspot data, the Mackey-Glass series data and the creeping displacement data of machine table were taken as numerical examples. General expression for nonlinear autoregressive(GNAR), back propagation(BP), RBF and RBF-BL models were used for data modeling and prediction. Mean squared error of modeling(MSEM), mean squared error of prediction(MSEP), mean relative error of modeling(MREM), and mean relative error of prediction(MREP)were taken as the error indicators. The results show that RBF-BL model exhibits better modeling and prediction performance compared with the traditional models. For the sunspot data, the error indicators of RBF-BL model are 0.009 6, 0.026 6, 0.002 7, and 0.003 9. For Mackey-Glass series data, the error indicators of RBF-BL model are 7.982×10-6, 6.400×10-4, 0.002 5, and 0.025 0. For the creeping displacement data of machine table, the error indicators of RBF-BL model are 7.590×10-4, 0.010 1, 0.038 8, and 0.023 8.

## 参考文献/References:

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