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Chaos time series prediction based on membrane optimization algorithms.

Li M, Yi L, Pei Z, Gao Z, Peng H - ScientificWorldJournal (2015)

Bottom Line: 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.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).

View Article: PubMed Central - PubMed

Affiliation: School of Radio Management Technology Research Center, Xihua University, Chengdu 610039, China.

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).

No MeSH data available.


Band occupancy rate data of interphone band.
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Related In: Results  -  Collection


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fig6: Band occupancy rate data of interphone band.

Mentions: In this paper, we adopt digital receiver EM100 which was provided by German Rohde & Schwarz Company and fixed radio monitoring station of Xihua University to collect data for the experiment. We collected data including frequency modulation (FM) broadcasting band and interphone band. As shown in Figures 5 and 6, in which the vertical axis denotes band occupancy rate, the horizontal axis represents the collection time, and left picture shows the data of band occupancy rate in FM broadcasting band, we collected for 680 hours, that is, obtaining 680 pieces of data. Right figure indicates acquisition data of band occupancy rate in interphone band; we continuously collected for 187 hours, that is, gaining 187 pieces of data. In order to facilitate narration, here we put the band data of FM broadcasting band and interphone band, denoted by “data set 1” and “data set 2,” respectively. Use the method of small amount of data to calculate the maximum Lyapunov index of two groups of data which are λ1 = 0.126 and λ2 = 0.14, respectively, which show the time series with chaos.


Chaos time series prediction based on membrane optimization algorithms.

Li M, Yi L, Pei Z, Gao Z, Peng H - ScientificWorldJournal (2015)

Band occupancy rate data of interphone band.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4385696&req=5

fig6: Band occupancy rate data of interphone band.
Mentions: In this paper, we adopt digital receiver EM100 which was provided by German Rohde & Schwarz Company and fixed radio monitoring station of Xihua University to collect data for the experiment. We collected data including frequency modulation (FM) broadcasting band and interphone band. As shown in Figures 5 and 6, in which the vertical axis denotes band occupancy rate, the horizontal axis represents the collection time, and left picture shows the data of band occupancy rate in FM broadcasting band, we collected for 680 hours, that is, obtaining 680 pieces of data. Right figure indicates acquisition data of band occupancy rate in interphone band; we continuously collected for 187 hours, that is, gaining 187 pieces of data. In order to facilitate narration, here we put the band data of FM broadcasting band and interphone band, denoted by “data set 1” and “data set 2,” respectively. Use the method of small amount of data to calculate the maximum Lyapunov index of two groups of data which are λ1 = 0.126 and λ2 = 0.14, respectively, which show the time series with chaos.

Bottom Line: 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.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).

View Article: PubMed Central - PubMed

Affiliation: School of Radio Management Technology Research Center, Xihua University, Chengdu 610039, China.

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).

No MeSH data available.