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fisher

于 2012-04-15 发布文件大小:1KB

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Fisher线性判别函数是研究线性判别函数中最有影响的方法之一。(Fisher linear discriminant function is one of the most influential in linear discriminant function.)

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randraw
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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XSLTWordDocument
THis application is demonstrate how to use XSLT sheets to make a word-document using OpenXML 2.0, WordProcessing.

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使用matalb软件，利用递推阻尼最小二乘法进行参数辨识，并改变噪声的强度，即 a*ξ(t)（0<a<1），分析、比较一般递推最小二乘和递推阻尼最小二乘方法的优缺点；(Use matalb software, the use of recursive damped least square method for parameter identification, and change the intensity of noise, that is a* ξ (t) (0 <a<1），分析、比较一般递推最小二乘和递推阻尼最小二乘方法的优缺点；)

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