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Neural_Networks_in_Finance

于 2007-03-16 发布文件大小:144KB

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Neural Network in Finance (神经网络在金融界:赢得预言性的优势)全部原码。内容包括预测与估计，对时间数列的分析，利用神经网络进行降维和分级等很多例子。(Neural Network in Finance (neural networks in the financial sector : won prophetic edge) all the original code. Including forecasts and estimates, the time series analysis, the use of neural network classification dimensionality reduction and many other examples.)

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WaveMatlab
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2008-10-17 09:39:59下载

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14
说明：层次分析法是对一些较复杂、较模糊的问题作出决策的简易方法，它特别适用于那些难于完全定量分析的问题。层次分析法是一种将决策者对复杂系统的决策思维过程模型化、数量化的过程。应用这种方法，决策者通过将复杂问题分解为若干层次和若干因素，在各因素之间进行简单的比较和计算，就可以得出不同方案的权重，为最佳方案的选择提供依据，因此层次分析法常应用分析评价定性量化问题。(Analytic Hierarchy Process is an easy way for some of the more complex, the more obscure the issue of decision-making, it is particularly difficult for those who complete quantitative analysis of the problem. Analytic Hierarchy Process is a method of decision-makers of complex systems thinking process modeling, quantitative process. Application of this method, decision makers through the complex problem into several levels and a number of factors, among the factors that make simple comparisons and calculations, different scenarios can be drawn on the right weight, provide the basis for selecting the best solution, so Analytic Hierarchy Process is often used to quantify the problem of qualitative analysis and uation.)

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MATLAB CODE TO COMPARE TWO IMAGES AND GET BACK THE ORIGINAL IMAGE IN THE RECEIVER END

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PSD of a sinusoidal signal

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小波分析理论与Matlab R2007实现(Wavelet analysis theory and Matlab R2007 implementation)

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fe_sdofID_2
Purpose: build the ID array of system dofs to apply displacement constraints Synopsis: [csdof,sid]=fe_sdofID(discons,ndof,nnode) Variable Description: csdof - the number of system dofs after constraints applied sid - ID matrix of system dof formatted as nnode*ndof discons - matrix record the node of displacement constrain ndof - number of dofs per node nnode - number of nodes in system(Purpose: build the ID array of system dofs to apply displacement constraints Synopsis: [csdof,sid]=fe_sdofID(discons,ndof,nnode) Variable Description: csdof- the number of system dofs after constraints applied sid- ID matrix of system dof formatted as nnode*ndof discons- matrix record the node of displacement constrain ndof- number of dofs per node nnode- number of nodes in system)

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EFFICIENT RANDOM VARIATES GENERATOR (from over 50 distributions):Alpha, Anglit, Antilognormal, Arcsin, Bernoulli, Bessel, Beta, Binomial, Bradford, Burr, Cauchy, Chi, Chi-Square (Non-Central), Chi-Square (Central), Cobb-Douglas, Cosine, Double-Exponential, Erlang, Exponential, Extreme-Value, F (Central), F (Non-Central), Fisher-Tippett, Fisk, Frechet, Furry, Gamma, Generalized Inverse Gaussian, Generalized Hyperbolic, Geometric, Gompertz, Gumbel, Half-Cosine, Hyperbolic Secant, Hypergeometric, Inverse Gaussian, Laplace, Logistic, Lognormal, Lomax, Lorentz, Maxwell, Nakagami, Negative Binomial, Normal, Normal-Inverse-Gaussian (NIG), Pareto, Pareto2, Pascal, Planck, Poisson, Quadratic, Rademacher, Rayleigh, Rice, Semicircle, Skellam, Student s-t, Triangular, Truncated Normal, Tukey-Lambda, U-shape, Uniform (continuous), Von Mises, Wald, Weibull, Wigner Semicircle, Yule, Zeta,(EFFICIENT RANDOM VARIATES GENERATOR (from over 50 distributions): Alpha, Anglit, Antilognormal, Arcsin, Bernoulli, Bessel, Beta, Binomial, Bradford, Burr, Cauchy, Chi, Chi-Square (Non-Central), Chi-Square (Central), Cobb-Douglas, Cosine, Double-Exponential, Erlang, Exponential, Extreme-Value, F (Central), F (Non-Central), Fisher-Tippett, Fisk, Frechet, Furry, Gamma, Generalized Inverse Gaussian, Generalized Hyperbolic, Geometric, Gompertz, Gumbel, Half-Cosine, Hyperbolic Secant, Hypergeometric, Inverse Gaussian, Laplace, Logistic, Lognormal, Lomax, Lorentz, Maxwell, Nakagami, Negative Binomial, Normal, Normal-Inverse-Gaussian (NIG), Pareto, Pareto2, Pascal, Planck, Poisson, Quadratic, Rademacher, Rayleigh, Rice, Semicircle, Skellam, Student s-t, Triangular, Truncated Normal, Tukey-Lambda, U-shape, Uniform (continuous), Von Mises, Wald, Weibull, Wigner Sem)

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emd
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Neural_Networks_in_Finance

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