Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing AlgorithmReportar como inadecuado




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Computational Intelligence and Neuroscience - Volume 2015 2015, Article ID 341031, 10 pages -

Research Article

National Key Laboratory on Electromagnetic Environmental Effects and Electro-Optical Engineering, PLA University of Science and Technology, Nanjing 210007, China

College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China

Received 15 October 2014; Revised 11 March 2015; Accepted 11 March 2015

Academic Editor: Francois B. Vialatte

Copyright © 2015 Jun Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing IGSA algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.





Autor: Jun Wang, Bi-hua Zhou, Shu-dao Zhou, and Zheng Sheng

Fuente: https://www.hindawi.com/



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