A novel chaotic time series prediction method and its application to carrier vibration interference attitude prediction of stabilized platform

• Main Authors: Hongbo Meng, Changming Wang, Aijun Zhang, Jiandong Bao
• Format: Article
• Language: English
• Published: JVE International 2016-02-01
• Series: Journal of Vibroengineering
• Subjects: chaos time series prediction; modified GM(1, 1) model; phase space reconstruction; optimizing parameters
• Online Access: https://www.jvejournals.com/article/16451

Aiming at the problems existing in previous chaos time series prediction methods, a novel chaos times series prediction method, which applies modified GM(1, 1) model with optimizing parameters to study evolution laws of phase point L1 norm in reconstructed phase space, is proposed in this paper. Phase space reconstruction theory is used to reconstruct the unobserved phase space for chaotic time series by C-C method, and L1 norm series of phase points can be obtained in the reconstructed phase space. The modified GM(1, 1) model, which is improved by optimizing background value and optimizing original condition, is used to study the change law of phase point L1 norm for forecasting. The measured data from stabilized platform experiment and three traditional chaos time series are applied to evaluate the performance of the proposed model. To test the prediction method, three accuracy evaluation standards are employed here. The empirical results of stabilized platform are encouraging and indicate that the newly proposed method is excellent in prediction of chaos time series of chaos systems.