A Method of Order Determination for ARX and ARMA Models Based on Nonnegative Garrote

Guo Dong Jin; Li Bin Lu; Xiao Fei Zhu

doi:10.4028/www.scientific.net/AMM.721.496

Paper Titles

Evaluation Framework and Method of the Intelligent Behaviors of Unmanned Ground Vehicles Based on AHP Scheme
p.476

Fracture Research on Preheating Installation of Directly Buried Heating Return Water Pipe with Large Diameter
p.481

The Electric Monitoring System Application Based on ZigBee Wireless Technology
p.486

Research on Magnetometer Positioning Technology Based on a Variable Magnetic Source
p.490

A Method of Order Determination for ARX and ARMA Models Based on Nonnegative Garrote
p.496

Research on Jamming Resource Scheduling Based on Multi-Target and Fuzzy Multi-Stage
p.500

The Design and Implementation of a Certain Type of Information Machine Simulator Based on ARM
p.505

PSO Based Feature Extraction Method for Analog Circuits Fault Information
p.509

A Precision Resistance Generator for the Calibration of RTD-Based Temperature Instruments
p.513

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 721A Method of Order Determination for ARX and ARMA...

A Method of Order Determination for ARX and ARMA Models Based on Nonnegative Garrote

Abstract:

Classical order determination methods of ARX and ARMA models suffer from the drawbacks of computationally infeasible and poor stability. To solve these problems, order determination using nonnegative garrote (NNG) method is proposed. By analyzing the properties of ARX and ARMA models, a modification of original NNG method is made to fit the dynamical system identification problem. Furthermore, the solution algorithm of proposed method is presented. Simulations show the validation of proposed method, which has better stability than classical information based criteria methods.

You might also be interested in these eBooks

Vehicle, Mechanical and Electrical Engineering

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 721)

Pages:

496-499

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.721.496

Citation:

Cite this paper

Online since:

December 2014

Authors:

Guo Dong Jin, Li Bin Lu, Xiao Fei Zhu

Keywords:

ARMA Model, ARX Models, Nonnegative Garrote, Order Determination

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Lujano-Rojas J M, Bernal-Agustin J L, Dufo-Lopez R, et al. Forecast of Hourly Average Wind Speed Using ARMA Model with Discrete Probability Transformation[J]. Electrical Engineering and Control, 2011; 98: 1003-1010.

DOI: 10.1007/978-3-642-21765-4_125

Google Scholar

[2] Cao Huilan1, Li Shanyou, LI Wei, Zhang Lei. Comparison among ARX structural modal parameter identification methods by using seism ic observation records [J]. Journal of Earthquake Engineering and Engineering Vibration, 2009; 29(2) : 144-150.

Google Scholar

[3] Venkatesh S. Modeling of a humidity system using ARX model and feed forward neural network-A comparative study[C]. Proc Int. Conf. on Advances in Engineering, Science and Management, Thanjavur, India, 2012: 518-522.

Google Scholar

[4] Zhang Xian-da. Modern signal processing [M]. Beijing: Tsinghua university press, (2002).

Google Scholar

[5] Wang Darong, Zhang Zhongzhan. Variable Selection for Linear Regression Models: A Survey[J]. Journal of Applied Statistics and Management, 2010; 29(4) : 615-627.

Google Scholar

[6] Peng Jialong, Wu Hong'e, Liu Cihua. Strong Consistent Estimation of Order for AR Model [J]. Journal of Kunming University of Science and Technology (Science and Technology), 2009; 34(3): 117-120.

Google Scholar

[7] Xing Mingzong, Zhao Fei, Jiang Gedong, Mei Xuesong. Novel Minimum Order Fixed Method for Autoregressive Moving Average Models[J]. Journal of Xi'an JiaoTong University, 2011; 45(12), 99-103.

Google Scholar

[8] De Ridder F, Pintelon R. Modified AIC and MDL Model Selection Criteria for Short Data Records[J]. IEEE Transactions on Instrumentation and Measurement, 2005; 54(1): 144-150.

DOI: 10.1109/tim.2004.838132

Google Scholar

[9] Breiman L. Better subset regression using the nonnegative garrote[J]. Technometrics, 1995; 37(4): 373-384.

DOI: 10.1080/00401706.1995.10484371

Google Scholar

[10] Ljung L. System Identification, Theory for the User[M]. New Jersey: Prentice Hall, (1999).

Google Scholar