title: Chaos time series prediction based on membrane optimization algorithms.
abstract: This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ, m) and least squares support vector machine (LS-SVM) (γ, σ) by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM) broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).
authors: School of Radio Management Technology Research Center, Xihua University, Chengdu 610039, China.
authors: Meng
authors: M
authors: Li
authors: School of Computer Science and Technology, Sichuan Police College, Luzhou 646000, China.
authors: Liangzhong
authors: L
authors: Yi
authors: School of Radio Management Technology Research Center, Xihua University, Chengdu 610039, China.
authors: Zheng
authors: Z
authors: Pei
authors: School of Radio Management Technology Research Center, Xihua University, Chengdu 610039, China.
authors: Zhisheng
authors: Z
authors: Gao
authors: School of Radio Management Technology Research Center, Xihua University, Chengdu 610039, China.
authors: Hong
authors: 0000-0002-4736-0164
authors: H
authors: Peng