基于蚁群算法的城市可持续发展综合评价   总被引：1，自引：0，他引：1  

徐婷婷  于扬  彭娇婷 《成都信息工程学院学报》2006,21(5):694-697

在可持续发展体系分类的基础上，应用蚁群算法对可持续发展各分类指数公式和综合指数公式进行优化，建立可持续发展评价模型。将该模型应用于乌鲁木齐市可持续发展水平评价，评价结果与现状分析一敛。结果表明该模型简单适用、有良好的通用性。  相似文献   

Galaxy spectral parametrization in the 2dF Galaxy Redshift Survey as a diagnostic of star formation history     

Darren S. Madgwick  Rachel Somerville  Ofer Lahav  Richard Ellis 《Monthly notices of the Royal Astronomical Society》2003,343(3):871-879

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分组密码的安全性与强化设计     

刘峰 《成都信息工程学院学报》2003,18(4):376-380

分组密码是数据通讯中最常用的数据加密方式，以DES为例分析现有分组加密算法的安全隐患，并提出了可变密钥加密和变长密文输出两个新思路，可应用于所有现有分组加密算法以提高安全性，并就该方法的安全性、效率、具体应用做出了分析。  相似文献   

On cross-correlating weak lensing surveys     

Dipak Munshi  Patrick Valageas 《Monthly notices of the Royal Astronomical Society》2005,356(2):439-455

The present generation of weak lensing surveys will be superseded by surveys run from space with much better sky coverage and high level of signal-to-noise ratio, such as the Supernova/Acceleration Probe ( SNAP ). However, removal of any systematics or noise will remain a major cause of concern for any weak lensing survey. One of the best ways of spotting any undetected source of systematic noise is to compare surveys that probe the same part of the sky. In this paper we study various measures that are useful in cross-correlating weak lensing surveys with diverse survey strategies. Using two different statistics – the shear components and the aperture mass – we construct a class of estimators which encode such cross-correlations. These techniques will also be useful in studies where the entire source population from a specific survey can be divided into various redshift bins to study cross-correlations among them. We perform a detailed study of the angular size dependence and redshift dependence of these observables and of their sensitivity to the background cosmology. We find that one-point and two-point statistics provide complementary tools which allow one to constrain cosmological parameters and to obtain a simple estimate of the noise of the survey.  相似文献   

Principal components in active galactic nuclei variability data and the estimation of the flux contributions from different components     

Chris Koen  Fred Lombard 《Monthly notices of the Royal Astronomical Society》2005,357(2):793-799

It has been found that the near-infrared flux variations of Seyfert galaxies satisfy relations of the form   F_i ≈α _{i j} +β _{i j} F_j   , where F_i , F_j are the fluxes in filters i and j ; and  α _{i , j} , β _{i , j}   are constants. These relations have been used to estimate the constant contributions of the non-variable underlying galaxies. The paper attempts a formal treatment of the estimation procedure, allowing for the possible presence of a third component, namely non-variable hot dust. In an analysis of a sample of 38 Seyfert galaxies, inclusion of the hot dust component improves the model fit in approximately half the cases. All derived dust temperatures are below 300 K, in the range 540–860 K or above 1300 K. A noteworthy feature is the estimation of confidence intervals for the component contributions: this is achieved by bootstrapping. It is also pointed out that the model implies that such data could be fruitfully analysed in terms of principal components.  相似文献   

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning     

Olac Fuentes 《Experimental Astronomy》2001,12(1):21-31

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.  相似文献   

Parameter information from non-linear cosmological fields     

A.N. Taylor  P.I.R. Watts † 《Monthly notices of the Royal Astronomical Society》2001,328(4):1027-1038

We develop a general formalism for analysing parameter information from non-Gaussian cosmic fields. The method can be adapted to include the non-linear effects in galaxy redshift surveys, weak lensing surveys and cosmic velocity field surveys as part of parameter estimation. It can also be used as a test of non-Gaussianity of the cosmic microwave background. Generalizing maximum-likelihood analysis to second order, we calculate the non-linear Fisher information matrix and likelihood surfaces in parameter space. To this order we find that the information content is always increased by including non-linearity. Our methods are applied to a realistic model of a galaxy redshift survey, including non-linear evolution, galaxy bias, shot-noise and redshift-space distortions to second order. We find that including non-linearities allows all of the degeneracies between parameters to be lifted. Marginalized parameter uncertainties of a few per cent will then be obtainable using forthcoming galaxy redshift surveys.  相似文献   

用遗传算法对自动控制系统数学模型寻优     

万月珍  廖成旺 《大地测量与地球动力学》2001,21(2):87-93

探讨用遗传算法对数学模型进行优化。考虑到控制系统稳健性的要求 ,用遗传算法寻找出控制系统最佳稳定域 ,实现控制系统数学模型的寻优。阐述了用遗传算法求解问题的步骤和参数的取值 ,并用仿真实验对优化结果进行了检验。结果表明 :在控制系统数学模型的优化中 ,遗传算法具有其他算法无可比拟的优越性。  相似文献   

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms   总被引：1，自引：0，他引：1  

J. Federico Ramírez  Olac Fuentes  Ravi K. Gulati 《Experimental Astronomy》2001,12(3):163-178

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.  相似文献   

Conditional cumulants in a weakly non-linear regime     

Jun Pan †  István Szapudi 《Monthly notices of the Royal Astronomical Society》2005,361(1):357-361

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