Structure, properties and unsupervised learning rules of neural networks with quadratic junction were presented. An ART-based hierarchical clustering algorithm using this kind of neural networks was suggested. The alg...
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Structure, properties and unsupervised learning rules of neural networks with quadratic junction were presented. An ART-based hierarchical clustering algorithm using this kind of neural networks was suggested. The algorithm can determine the number of clusters and clustering data. Simulation experiment shows proposed algorithm is more effective than the K- medoids algorithm.
In this paper we present a generalization of the classic Firm's Profit Maximization Problem, using the linear model for the production function, considering a decreasing price w(i)(x(i)) = b(i) - c(i)x(i) and maxi...
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In this paper we present a generalization of the classic Firm's Profit Maximization Problem, using the linear model for the production function, considering a decreasing price w(i)(x(i)) = b(i) - c(i)x(i) and maximum constraints for the inputs or, equivalently, considering inputs that are in turn outputs in economies of scale with quadratic concave cost functions. We formulate the problem by previously calculating the analytical minimum cost function in the quadratic concave case. This minimum cost function will be calculated for each production level via the infimal convolution of quadratic concave functions whose result is a piecewise quadratic concave function. (C) 2012 Elsevier B.V. All rights reserved.
In this paper we present an algorithm of quasi-linear complexity to exactly calculate the infimal convolution of convex quadratic functions. The algorithm exactly and simultaneously solves a separable uniparametric fa...
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In this paper we present an algorithm of quasi-linear complexity to exactly calculate the infimal convolution of convex quadratic functions. The algorithm exactly and simultaneously solves a separable uniparametric family of quadratic programming problems resulting from varying the equality constraint. (C) 2011 Elsevier B.V. All rights reserved.
In this paper we present a generalization of the classic Firm's Profit Maximization Problem, using the linear model for the production function, considering a decreasing price w(i)(x(i)) = b(i) - c(i)x(i) and maxi...
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In this paper we present a generalization of the classic Firm's Profit Maximization Problem, using the linear model for the production function, considering a decreasing price w(i)(x(i)) = b(i) - c(i)x(i) and maximum constraints for the inputs or, equivalently, considering inputs that are in turn outputs in economies of scale with quadratic concave cost functions. We formulate the problem by previously calculating the analytical minimum cost function in the quadratic concave case. This minimum cost function will be calculated for each production level via the infimal convolution of quadratic concave functions whose result is a piecewise quadratic concave function. (C) 2012 Elsevier B.V. All rights reserved.
In this paper we present an algorithm of quasi-linear complexity to exactly calculate the infimal convolution of convex quadratic functions. The algorithm exactly and simultaneously solves a separable uniparametric fa...
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In this paper we present an algorithm of quasi-linear complexity to exactly calculate the infimal convolution of convex quadratic functions. The algorithm exactly and simultaneously solves a separable uniparametric family of quadratic programming problems resulting from varying the equality constraint. (C) 2011 Elsevier B.V. All rights reserved.
This study improves the neural network algorithm that was presented by *** for solving TSP(travelling salesman problem) and gets an effective algorithm whose time complexity is O(n(2)), so we can solve quickly TSP mor...
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ISBN:
(纸本)9783037854488
This study improves the neural network algorithm that was presented by *** for solving TSP(travelling salesman problem) and gets an effective algorithm whose time complexity is O(n(2)), so we can solve quickly TSP more than 500 cities in microcomputer. The paper considers the algorithm based on the replacement function of the V Value. The improved algorithm can greatly reduces the time and space complexities of Hopfield method. The TSP examples show that the proposed algorithm could efficiently find a satisfactory solution and has a fast convergence speed.
The paper describes the basic concepts of rough set theory and discernibility matrix and presents an attribute reduction algorithm based on reduced discernibility matrix,which aims at resolving the inadequate of the e...
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The paper describes the basic concepts of rough set theory and discernibility matrix and presents an attribute reduction algorithm based on reduced discernibility matrix,which aims at resolving the inadequate of the existing attribute reduction based on incomplete decision *** only contain useful elements for the algorithm in the reduced discernibility matrix,which obtain one reduction of incomplete decision table by iteration and set *** experimental results show that the algorithm can not only obtain reduced attribute,but also reduce the computation time and storage space greatly.
Although the ensemble empirical mode decomposition (EEMD) method and Hilbert-Huang transform (HHT) offer an unrivaled opportunity to understand neural signals, the EEMD algorithm's complexity and neural signals...
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Although the ensemble empirical mode decomposition (EEMD) method and Hilbert-Huang transform (HHT) offer an unrivaled opportunity to understand neural signals, the EEMD algorithm's complexity and neural signals' massive size have hampered EEMD application. However, a new approach using a many-core platform has proven both efficient and effective for massively parallel neural signal processing.
Purpose - Orthophoto suffers from the relief displacement effects magnified by high resolution imaging sensors especially when mapping urban areas. True orthophotos eliminating relief displacement with digital surface...
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Purpose - Orthophoto suffers from the relief displacement effects magnified by high resolution imaging sensors especially when mapping urban areas. True orthophotos eliminating relief displacement with digital surface model (DSM) are presented to assure reliable interpretability and maintain the high quality of the available data. Previous efforts did not provide accurate and fast ways for generating true othorphoto. The purpose of this paper is to try to solve the problem by analyzing the complexity of algorithm processes and finding the optimum manner to allocate them. Design/methodology/approach - In this paper, an optimum segmentation number for radial sweep is presented to achieve minimum complexity. First, the scan area, number of azimuth lines and visibility judgment area of radial sweep and spiral sweep method have been discussed with rigorous geometric theory, and then algorithm complexities of both methods are estimated with mathematical computation theory. Finally, minimum complexity of the methods is obtained with extreme point theory of differential calculus. Findings - Experiments have demonstrated that the proposed method has the best efficiency, and is efficient to avoid "M-potion" problem, and false occlusions and false visibilities caused by the rolling area, the incompatibility between the DSM and ground image resolution. Originality/value - The deduction and experiments indicate that the proposed method is a robust, accurate, fast, and effective approach to generate high quality, true orthophoto at a large-scale.
A new procedure is introduced for conducting screening experiments to find a small number of influential factors from among a large number of factors with negligible effects. It is intended for experiments in which th...
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A new procedure is introduced for conducting screening experiments to find a small number of influential factors from among a large number of factors with negligible effects. It is intended for experiments in which the factors are easily controlled, as in simulation models. It adds observations sequentially after conducting a small initial experiment. The performance of the procedure is investigated using simulation, and evidence is presented that this and other procedures scale as the logarithm of the total number of factors if the number of influential factors is fixed. An investigation of the new procedure for 1-3 active factors shows that it compares favorably with competing methods, particularly when the size of the nonzero effects is 1-2 times the standard deviation. A limited look at the procedure for up to 6 active factors is also presented.
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