A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting. (23rd March 2015)

• Record Type: Journal Article
• Authors: Jia, Chaolong; Wei, Lili; Wang, Hanning; Yang, Jiulin
• Other Names: Chen Hong Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2015(2015)
• Publication Date: 2015-03-23
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2015/548720
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.