A New Algorithm for Long-Term Estimation Based on AR Model

Wen Jun Su; Hai Tao Chen

doi:10.4028/www.scientific.net/AMM.614.440

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 614A New Algorithm for Long-Term Estimation Based on...

A New Algorithm for Long-Term Estimation Based on AR Model

Abstract:

Traditional estimation methods have poor performance for long-term data forecast. Using Wiener model to estimate, power spectral density of the input signal, and cross-spectral density of the input and output signals are needed, that are difficult to obtain. And the large amount of calculation is needed using Wiener model. Using AR model and Kalman model, estimated results tend to mean of the training set while the estimated distance increases. For these cases, a new algorithm for long-term estimation based on AR model, named sampling AR model, is presented. Grouping the training set and using a different group of the training set to estimate each value. Sampling AR model improves the accuracy of long-term estimation.

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Periodical:

Applied Mechanics and Materials (Volume 614)

Pages:

440-443

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.614.440

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Online since:

September 2014

Authors:

Wen Jun Su*, Hai Tao Chen

Keywords:

AR Model, Kalman Model, Long-Term Estimation, Sampling AR Model, Wiener Model

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* - Corresponding Author

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