In this paper, the Crank-Nicolson (CN) difference scheme for the coupled nonlinear Schrödinger equations with the Riesz space fractional derivative is studied. The existence of this difference solution is proved ...
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Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theor...
Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. Some algorithms for MFA of unweighted complex networks have been proposed in the past a few years, including the sandbox (SB) algorithm recently employed by our group. In this paper, a modified SB algorithm (we call it SBw algorithm) is proposed for MFA of weighted networks. First, we use the SBw algorithm to study the multifractal property of two families of weighted fractal networks (WFNs): "Sierpinski" WFNs and "Cantor dust" WFNs. We also discuss how the fractal dimension and generalized fractal dimensions change with the edge-weights of the WFN. From the comparison between the theoretical and numerical fractal dimensions of these networks, we can find that the proposed SBw algorithm is efficient and feasible for MFA of weighted networks. Then, we apply the SBw algorithm to study multifractal properties of some real weighted networks - collaboration networks. It is found that the multifractality exists in these weighted networks, and is affected by their edge-weights.
Bacterial Foraging Optimization(BFA) algorithm has recently emerged as a very powerful technique for real parameter optimization, but the E. coli algorithm depends on random search directions which may lead to delay i...
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In order to accomplish tracking of moving objects requirements, and overcome the defect of occlusion in the process of tracking moving object, this paper presents a method which uses a combination of MeanShift and Kal...
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Turbo codes have a wide range of applications in 3G mobile communications, deep-sea communications, satellite communications and other power constrained fields. In the paper, the Turbo Code Decoding Principle and seve...
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Gene selection is an important research topic in pattern recognition and tumor classification. Numerous methods have been proposed, Maximum Margin Criterion (MMC) is one of the famous methods have been proposed to sol...
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This paper use the characteristics of text in the email text categorization propose a method of feature word extraction based on many-objective evolutionary algorithms. This method fully considers the semantic charact...
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This paper is devoted to investigating the numerical solution for a class of fractional diffusion-wave equations with a variable coefficient where the fractional derivatives are described in the Caputo sense. The appr...
Though K-L transform is an optimum transform for image compression based on minimum mean-squared error, its matrix transform differentiates according to the images and the calculation is heavy and difficult. Some pape...
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Dynamic programming(DP) is not a useful tool for solving many control problems because of its complexity in computation. In this paper,we propose Approximate Dynamic Programming(ADP) optimal control strategy for ship ...
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ISBN:
(纸本)9781479900305
Dynamic programming(DP) is not a useful tool for solving many control problems because of its complexity in computation. In this paper,we propose Approximate Dynamic Programming(ADP) optimal control strategy for ship course trajectory tracking control *** system transformation,we convert the optimal tracking problem into designing a infinite-horizon optimal regulator for the tracking error ***-dependent Heuristic Dynamic programming(ADHDP) technique,as one form of ADR is presented to obtain the infinite-horizon optimal tracking *** the ship course optimal tracking control simulation results,we can see that the ADHDP controller makes the performance index and the control sequence for the error dynamics converge to the optimal *** BP neural networks are used as parametric structures to implement ADHDP *** two neural networks aim at approximating the cost function and the control law,respectively.
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