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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.


Five models predicted diagram for interphone band.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig9: Five models predicted diagram for interphone band.

Mentions: Selecting the first point as input to obtain first predicted value, then the real value of the first point is added to the historical data, predicting the next point. And so, obtain the predicted value of all points. Prediction results of five models are shown in Tables 3, 4, 5, 6, 7, and 8 and Figures 7, 8, 9, and 10.


Chaos time series prediction based on membrane optimization algorithms.

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

Five models predicted diagram for interphone band.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig9: Five models predicted diagram for interphone band.
Mentions: Selecting the first point as input to obtain first predicted value, then the real value of the first point is added to the historical data, predicting the next point. And so, obtain the predicted value of all points. Prediction results of five models are shown in Tables 3, 4, 5, 6, 7, and 8 and Figures 7, 8, 9, and 10.

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.