Optimal Design of Cognitive Radio Wireless Parameters based on Non-dominated Neighbor Distribution Genetic Algorithm

Ma, Jinghui; Jiang, Hong; Hu, Yongchun; Bao, Yujun

doi:10.1109/icis.2009.18

Cited by 4 publications

(3 citation statements)

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“…To our best known, the GA and REF _ NN for dealing end effects of HHT have not been reported. Taking into account the current solutions for the problem in REF _ NN and the search capability of GA, this paper uses multi-objective GA to select the optimal parameters [14]. The real-time and prediction accuracy are considered to select the parameters with utility function.…”

Section: =1mentioning

confidence: 99%

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Notice of Retraction: End effects processing of Hilbert-Huang transform based on genetic algorithm and RBF Neural Network

Jiang

Yao

et al. 2010

2010 3rd International Conference on Computer Science and Information Technology

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AmIMcl-The problem of end effects in Hilbert-Huang transform is produced in the Empirical Mode Decomposition (EMD), which has a badly effect on HUbert-Huang transform. In order to overcome this problem, multi-objective Genetic Algorithm (GA) for solving the parameters selection of RBF Neural Network (RBF_NN) (GRHHT) is presented in tbis paper. Then the RBF_NN is used to predict the signal before EMD. The scheme can effectively resolve the end effects. The simulation results from the typical definite signals demonstrate that the problem of end effects in HUbert Huang transform could be resolved effectively, and its performance is better than prediction methods by RBF neural network and support Vector Machine (SVM), respectively.Keywo1Yls-genetic IIIgoritlt ll l, ' HillJ ert-HllfIII g hYmsfonn,'nelllYli network,' slIjJport vector lII f1cltin 1.

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Section: =1mentioning

confidence: 99%

“…Step 6: Proportional distribution, the distribution number around each individual can be calculated by [14]. Generate distribution population Dt by proportional distribution for every individual.…”

Section: =1mentioning

confidence: 99%

Notice of Retraction: End effects processing of Hilbert-Huang transform based on genetic algorithm and RBF Neural Network

Jiang

Yao

et al. 2010

2010 3rd International Conference on Computer Science and Information Technology

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“…When the training samples are determined, the model parameters of LSSVM are trained by multi-objective GA [17]. According to the efficiency of the algorithm, the parameters which have the less number of training samples and smaller LSSVM prediction error are selected by multi-objective GA.…”

Section: B Multi-objective Gamentioning

confidence: 99%

Research of end effects in Hilbert-Huang transform based on genetic algorithm and support vector machine

Jiang

et al. 2010

The 3rd International Conference on Information Sciences and Interaction Sciences

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The end effects of Hilbert-Huang transform are produced in the Empirical Mode Decomposition(EMD) and the Hilbert transform for Intrinsic Mode Functions(IMF), which have a badly effect on Hilbert-Huang transform. In order to overcome this problem, the multi-objective allocation Genetic Algorithm (GA) to solve the kernel parameters selection of Least Squares Support Vector Machine (LSSVM)(GLHHT) is presented in this paper. Then the LSSVM is used to predict the signal before EMD. The scheme can effectively resolve the end effects, and obtain the IMFs with explicitly physical sense and Hilbert spectrum. The simulation results from the typical definite and practical signals demonstrate that the end effects of Hilbert Huang transform could be resolved effectively, and its effects are better than prediction methods by RBF neural network and SVM, respectively.

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2012

Cognitive Radio Communications and Networking

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Optimal Design of Cognitive Radio Wireless Parameters based on Non-dominated Neighbor Distribution Genetic Algorithm

Cited by 4 publications

References 1 publication

Notice of Retraction: End effects processing of Hilbert-Huang transform based on genetic algorithm and RBF Neural Network

Notice of Retraction: End effects processing of Hilbert-Huang transform based on genetic algorithm and RBF Neural Network

Research of end effects in Hilbert-Huang transform based on genetic algorithm and support vector machine

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