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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.


Membrane structure.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig4: Membrane structure.

Mentions: As shown in Figure 4, this paper adopts two layers structure for membrane, a skin contains B basic membrane, generate initial objects in each membrane. Generally, p system uses character or character string to encode, real number encoding are adopted in here, which can reduce the trouble of decode. For instance, O = (o1, o2, o3, o4), where O is an object and o1, o2, o3, and o4 denote τ, m, γ, and σ, respectively. We see each object as a solution of the optimization problem. Evolution of each membrane according to its own rules, all the membrane are executed in parallel. The final optimal results are output through the skin, that is, the optimal solution.


Chaos time series prediction based on membrane optimization algorithms.

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

Membrane structure.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Membrane structure.
Mentions: As shown in Figure 4, this paper adopts two layers structure for membrane, a skin contains B basic membrane, generate initial objects in each membrane. Generally, p system uses character or character string to encode, real number encoding are adopted in here, which can reduce the trouble of decode. For instance, O = (o1, o2, o3, o4), where O is an object and o1, o2, o3, and o4 denote τ, m, γ, and σ, respectively. We see each object as a solution of the optimization problem. Evolution of each membrane according to its own rules, all the membrane are executed in parallel. The final optimal results are output through the skin, that is, the optimal solution.

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.