蚁群算法

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)

LSSVM classify最小支持向量机分类程序(LSSVM classify)

微小型MEMS陀螺的误差特性研究，对于mems陀螺仪误差建模和补偿方法(Miniature MEMS gyro error characteristics of the MEMS gyroscope error modeling and compensation method)

说明：  Kalman filter word document

在simulink里如何精确建模－好文推荐 Building Accurate, Realistic Simulink Models By Seth Popinchalk An accurate system model is one that performs as expected and produces the desired results. To achieve this level of accuracy you must have a clear modeling goal. For example, will the model be used strictly for simulation, or for generating embedded C code?? This article describes five modeling techniques that can help ensure accuracy: Select the right modeling approach Know your system, understand its dynamics Monitor fo (How accurate modeling in simulink- good text recommended)

用toplitze 矩阵重构法实现相干信源来波方向估计(using the method of matrix reconsructing to realize the estimation of DOA of coherent singnal)

Gauss-seidel迭代与两种Jacobi迭代的matlab实现(Gauss-seidel iterative realization of the matlab)

matlab在数学建模中的应用 第一章案例一(application in modeling with matlab chapter one case one)

在Matlab中写的Lattice Boltzmann程序,对LBM学习者很有用。(LB programing with Matlab.)

压缩感知方面的17篇综述性文件Rice大学(review of compression sensing 17)