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包含了数值计算的绝大部分程序，依照英文可以查找级程序(Contains most of the numerical calculation procedure can find level program in accordance with English)

内含结构拓扑优化的众多数学规划方法，其中包含最小二乘法、移动渐近线法等等(The file includes many methods for topology optimization，such as the method of moving asymptotes.)

说明：  移动通信系统 信道，接收机等的MATLAB源代码，较详细(mobile communications MATLAB demo)

用matlab的核函数对钓鱼岛实现分类（模式识别作业）(Kernel function using matlab realize the Diaoyu Islands classification (pattern recognition operations))

SUBTRACTIVE CLUSTERING ALGORITHM

说明：  此文件里包含了关于Matlab的经典程序，对于Matlab的编程有极大帮助，包括差值拟合等等，另附上一个实时时钟的Matlab程序，也比较经典。(This file contains the classical procedures on Matlab, Matlab programming for the great help, including the difference between the fitting and so on, attached to a real-time clock of the Matlab program, but also more classic.)

matlab中的psat软件可以用于潮流计算，连续潮流计算，小扰动分析，分岔分析等等，这是三个版本的说明书(psat matlab software can be used in the flow calculation, continuous flow calculation, the small perturbation analysis, bifurcation analysis, etc., which are the three versions of the manual)

针对基本快速扩展随机树(RRT)算法存在搜索过于平均、效率低下、用时较长的缺陷，提出了一种偏向目标型的改进型RRT算法。这种算法在生成随机点时以一定概率选择最终目标点作为局部目标点，使树的扩展有一个趋向于最终目标点的趋势，从而加快了算法的收敛速度，优化了规划路径(For basic fast Random Tree (RRT) search algorithm exists too mean, inefficient, long time with defects, we proposed a goal-biased improved RRT algorithm. This algorithm when generating random points with a certain probability of selecting a final destination point as a local target, enabling the expansion of the tree has a tendency to trend the final destination point, thereby speeding up the convergence of the algorithm, optimized route planning)

捷联惯导的C程序实现，四元数表示姿态，动态数据显示，MATLAB进行图像处理，包含原始数据。(SINS of the C program implementation, said the attitude quaternion, dynamic data, MATLAB for image processing, including raw data.)

randgen(mu,mu1,mu2,cov1,cov2,cov3) = Random generation of Gaussian Samples in d-dimensions where d = 2 mu, mu1, mu2 = (x,y) coordinates(means) that the gaussian samples are centered around cov1, cov2, cov3 are the covariance matrices and will vary changing the shape of the distribution, example: cov = sigma^2*Identity Matrix, where sigma^2 = a scalar N = the number of gaussian samples used are provided as user input, A test set of N/2 and a training set of N/2 gaussian samples is also generated Output is directed to the command window and a plot of the distributions are generated