ScopeMath

根据邻接矩阵计算网络的聚类系数。输入为邻接矩阵输出为整个网络的聚类系数(cluster coefficient)

说明：   已知某控制系统结构图如下所示,要求设计校正环节G(s),使系统对于阶跃输入的稳态误差为零。使系统校正后的相角裕量γ≥45°，幅值裕量kg≥10db。(Known structure of a control system as shown below, links to design correction G (s), allowing the system to step input for the steady-state error to zero. The system after correction phase margin γ ≥ 45 °, the amplitude margin kg ≥ 10db.)

基本蜂群的另一种实现形式~~~~~~~~~~~~~~~~~~(basic ABC)

对libsvm 的结构参数、核函数、svm预测以及训练等几个方面进行了详细的分析(It s detailed analysis Libsvm of the structural parameters and kernel function, SVM forecasting and training and so on.)

Hi This mfile is source code for image segementation using active contour models [Snake]. It found the objects in imaages.

Matlab语音处理工具箱，很多语音处理m文件(Speech Processing Toolbox Matlab, many voice processing document m)

对应不同的a值产生一组矢量来表示高斯脉冲波形，并计算其相应的ESD。最后，画出了各个a值所对应的脉冲波形及其ESD(a different corresponding values of a group vector to express Gaussian pulse waveform, and calculated the corresponding ESD. Finally, paint all of a value corresponding to the pulse waveform and ESD)

丹麦工业大学FIELD声场仿真工具包，matlab编写(Denmark Industrial University FIELD sound field simulation tool kits prepared Matlab)

hough变换的检测函数，调试成功的，可以用，对于圆检测很有帮助，大家可以学习一下(hough transform function testing, debugging successful, you can use, very helpful for circle detection, we can learn about)

％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％( EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates)