[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]
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RBF-BL时间序列模型及其在建模和预测中的应用()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
50
期数:
2020年第2期
页码:
368-376
栏目:
计算机科学与工程
出版日期:
2020-03-20

文章信息/Info

Title:
RBF-BL time series model and its application in modeling and prediction
作者:
李博伟许飞云杨会超
东南大学机械工程学院, 南京 211189
Author(s):
Li Bowei Xu Feiyun Yang Huichao
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
关键词:
非线性模型 参数辨识 建模 预测
Keywords:
nonlinear model parameter identification modeling prediction
分类号:
TP391
DOI:
10.3969/j.issn.1001-0505.2020.02.022
摘要:
基于传统的线性和非线性模型,提出了一种改进的非线性模型,即RBF神经网络的状态依赖双线性(RBF-BL)模型.以模型残差平方和最小为优化目标,介绍了模型参数辨识算法.以太阳黑子数据、Mackey-Glass序列数据和机床工作台爬行位移数据为数值算例,基于GNAR模型、BP模型、RBF模型和RBF-BL模型分别进行了数据建模和预测,以建模均方误差(MSEM)、预测均方误差(MSEP)、建模平均相对误差(MREM)和预测平均相对误差(MREP)作为误差衡量指标.结果表明,与传统的模型相比,RBF-BL模型表现出较好的建模和预测性能.对于太阳黑子数据,RBF-BL模型的误差衡量指标分别为0.009 6、0.026 6、0.002 7和0.003 9.对于Mackey-Glass序列数据,RBF-BL模型的误差衡量指标分别为7.982×10-6、6.400×10-4、0.002 5和0.025 0.对于机床工作台爬行位移数据,RBF-BL模型的误差衡量指标分别为7.590×10-4、0.010 1、0.038 8和0.023 8.
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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备注/Memo

备注/Memo:
收稿日期: 2019-07-06.
作者简介: 李博伟(1983—),男,博士生;许飞云(联系人),男,博士,教授,博士生导师,fyxu@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(51305176,51575101).
引用本文: 李博伟,许飞云,杨会超.RBF-BL时间序列模型及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.
更新日期/Last Update: 2020-03-20