# [1]马家欣,许飞云,黄仁.一种线性/非线性自回归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),2013,43(3):509-514.[doi:10.3969/j.issn.1001-0505.2013.03.012] 　Ma Jiaxin,Xu Feiyun,Huang Ren.A linear and nonlinear auto-regressive model and its application in modeling and forecasting[J].Journal of Southeast University (Natural Science Edition),2013,43(3):509-514.[doi:10.3969/j.issn.1001-0505.2013.03.012] 点击复制 一种线性/非线性自回归模型及其在建模和预测中的应用() 分享到： var jiathis_config = { data_track_clickback: true };

43

2013年第3期

509-514

2013-05-20

## 文章信息/Info

Title:
A linear and nonlinear auto-regressive model and its application in modeling and forecasting

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

Keywords:

TP391
DOI:
10.3969/j.issn.1001-0505.2013.03.012

Abstract:
To improve the model accuracy, a linear and nonlinear auto-regressive model with exogenous inputs is proposed based on classical time series modeling strategy. And, with the Weierstrass approximation theorem, the general expression for the linear and nonlinear auto-regressive model with exogenous inputs(GNARX)is deduced. As multiple exogenous inputs are allowable, this model can realize modeling and identification of complex systems. Furthermore, concerning the model structure, a least square parameter estimation method is presented. With modified information criterion(AIC)integrated with modeling error, forecasting error and model complexity, the optimal model is determined. Finally, this model is applied to the modeling and forecasting of simulation data and the current sampling data of vibration displacement. The results show that the modeling and forecasting accuracy of the GNARX model is higher than those of AR, GNAR, ARX models and BP neural network, indicating that the GNARX model has good linear and nonlinear modeling ability and forecasting ability with good universality and practical value.

## 参考文献/References:

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