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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.


The flow chart of chaotic time series prediction.
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Related In: Results  -  Collection


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fig1: The flow chart of chaotic time series prediction.

Mentions: LS-SVM learning performance is largely dependent on the choice of kernel function. A large number of studies have shown that, with the lack of a priori knowledge of specific issues, the overall performance of the radial basis kernel function model is better than other kernel function models and hence this paper selects the radial basis kernel function as the kernel function of LS-SVM. So in the model, there are two parameters (cost factor (γ) and kernel parameter (σ)) that need to be identified; cost factor γ is generally used to control the model complexity and compromise of approximation error, which is commonly in [1,1000]. Kernel parameter σ reflects the structure of high-dimensional feature space and affects the generalization ability of the system; when the value of σ is too small, it will occur over-learning phenomenon and poor generalization, while the value of σ is too large, it will emerge less learning phenomenon; the range of σ is in [0.1,10000] [22]. Currently, there are mainly two ideas for optimization of the parameters of the phase space reconstruction (τ, m) and LS-SVM (γ, σ). One is that the parameters were optimized separately as shown in Figure 1, in which, firstly, optimal delay time (τ) and embedding dimension (m) in the phase space are selected independently [19, 23–28] or at the same time [27, 29, 30]; then parameters γ and σ of the LS-SVM are selected by gradient descent method [31], genetic algorithm (GA) [32] or particle swarm optimization (PSO) [33], and so forth. Another idea is to optimize jointly the parameters, that is, the parameters (τ, m, γ, σ) as a whole to carry on the optimization [34].


Chaos time series prediction based on membrane optimization algorithms.

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

The flow chart of chaotic time series prediction.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: The flow chart of chaotic time series prediction.
Mentions: LS-SVM learning performance is largely dependent on the choice of kernel function. A large number of studies have shown that, with the lack of a priori knowledge of specific issues, the overall performance of the radial basis kernel function model is better than other kernel function models and hence this paper selects the radial basis kernel function as the kernel function of LS-SVM. So in the model, there are two parameters (cost factor (γ) and kernel parameter (σ)) that need to be identified; cost factor γ is generally used to control the model complexity and compromise of approximation error, which is commonly in [1,1000]. Kernel parameter σ reflects the structure of high-dimensional feature space and affects the generalization ability of the system; when the value of σ is too small, it will occur over-learning phenomenon and poor generalization, while the value of σ is too large, it will emerge less learning phenomenon; the range of σ is in [0.1,10000] [22]. Currently, there are mainly two ideas for optimization of the parameters of the phase space reconstruction (τ, m) and LS-SVM (γ, σ). One is that the parameters were optimized separately as shown in Figure 1, in which, firstly, optimal delay time (τ) and embedding dimension (m) in the phase space are selected independently [19, 23–28] or at the same time [27, 29, 30]; then parameters γ and σ of the LS-SVM are selected by gradient descent method [31], genetic algorithm (GA) [32] or particle swarm optimization (PSO) [33], and so forth. Another idea is to optimize jointly the parameters, that is, the parameters (τ, m, γ, σ) as a whole to carry on the optimization [34].

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.