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三相短路计算程序，计算任意地点三相短路后各支路电流和各节点电压(Three phase short circuit calculation program-After the three-phase short circuit in arbitrary locations, the current of each branch and the voltage of each node can be abtained.)

ahp的matlab程序，输入矩阵即可使用(ahp matlab program, you can use the input matrix)

HILBERTAVERAGE PITCH CALCULATION

维纳滤波算法MATLAB实现，能对数据进行维纳滤波(MATLAB realization of Wiener filtering algorithm, Wiener filter to the data)

说明：  如果对实际工程结构作简化处理，建立其动力学模型，并确定系统的质量、刚度、阻尼参数，便可以用多种方法建立系统的动力微分方程。常用的方法有直接 法、影响系数法、拉格朗日法以及有限元法等。拉格朗日法是从能量的观点建立系统的动能T、势能U和功W之间的标量关系，是研究静、动力学问题的一种方法。如果在选定运动坐标以后，能够设法求得坐标相对应的质量矩阵和刚度矩阵，就可以用影响系数法建立多自由度系统运动微分方程[4]。本文采用拉格朗日法建立了1/4车辆几何模型。(If the actual engineering structure is simplified, its dynamic model is established, and the mass, stiffness and damping parameters of the system are determined, the dynamic differential equation of the system can be established by various methods. The commonly used methods are direct method, influence coefficient method, Lagrange method and finite element method. Lagrange method is a method to study the static and dynamic problems, which is to establish the scalar relation among kinetic energy t, potential energy u and work W from the point of view of energy. If the mass matrix and stiffness matrix corresponding to the coordinates can be obtained after the motion coordinates are selected, the differential equation of motion of the multi degree of freedom system can be established by the influence coefficient method [4]. In this paper, 1 / 4 vehicle geometry model is established by Lagrange method.)

光伏并网发电系统中逆变器的研究与设计,比较有用的资料，逆变器设计可以参考(Grid-connected PV system inverter design, useful information, inverter design can refer)

说明：  利用matlab实现流体力学中经典算例：二维粘性方腔不可压流动的计算。(Matlab is used to calculate the incompressible flow in a two-dimensional viscous square cavity.)

此Matlab程序中包含两种定位算法，一种为最小二乘法，另一种为加权最小二乘法。(This Matlab program contains two location algorithm, a least-squares method, the other is weighted least squares method.)

说明：  matlab语言编写的实现iris数据的最小错误贝叶斯分类(Iris data to achieve the minimum error Bayesian classification)

input: param: parameters of the LMGE algorithm param.mu, param.alpha, param.beta are regularization parameters. param.p: dimension of shared subspace param.k: number of nearest neighbors for Laplacian matrix X: input data Y: groundtruth labels output: model.W: matrix W Reference: Web and Personal Image Annotation by Mining Label Correlation with Relaxed Visual Graph Embedding Yi Yang, Fei Wu, Feiping Nie, Heng Tao Shen, Yueting Zhuang and Alex Hauptmann. contact: yyang@cs.cmu.edu (Web and Personal Image Annotation by Mining Label Correlation with Relaxed Visual Graph Embedding )