two

用matlab语言写的C4.5算法,用于模式分类(Matlab language used to write the C4.5 algorithm for pattern classification)

自己写的模糊PID的simulink文件(simulink fuzzy PID)

Implemenation of lms algorithm

该编程为矩阵与数据分析课的编程作业程序，里面包括matlab的三次样条曲线程序，有Jacobi迭代法和Gauss-Seidel迭代法等(The programming and data analysis for the matrix class programming operations program, which includes the cubic spline curve matlab procedures, Jacobi and Gauss-Seidel iterative method iterative method)

王兴刚和白翔合作发表的CVPR2011的文章， Feature Context for Object Detection and Image Classification，可以用于物体检测和图像的分类，用到了特征上下文，Feature Context(PROCEEDINGS and white Xiang cooperation the published CVPR2011 article Feature Context for Object Detection and Image Classification can be used for object detection and classification of the image, to use the features of the context, Feature Context)

bsg.c - block layer implementation of the sg v4 interface.

第三代移动通信多输入多输出系统空时分组码matlab系统仿真源代码(Third generation mobile communication system, multi-input multi-output space-time block code system simulation matlab source code for)

Sequentielle Quadratische Optimierung fuer f(x) = Min!, g(x) >= 0, h(x) = 0 unter Regularitaetsbedingung FUNKTIONEN: gp.m, gp_g.m, restor.m, sigini.m name_f.m: Zielfunktion/Gradient name_g.m: Ungleichunen/Gradienten name_h.m: Gleichungen/Gradienten INPUT: name Name des Problems x zulaessiger Punkt als Startvektor OUTPUT: (x,y,z) Kuhn-Tucker-Punkt f = f(x) Funktionswert errorcode = 1: Max. Schrittzahl in Iteration errorcode = 2: Max. Schrittzahl in Backtracking errorcode = 3: x nicht zulaessig

实现数字网络模拟过程的matlab程序。在matlab上调试通过(Digital network simulation matlab process procedures. Debugging through the matlab)

适当选择辅助变量，使之满足相应条件，参数估计值就可以是无偏一致。估计辅助变量法的计算量与最小二乘法相当，但辨识效果却比最小二乘法好的多。尤其当噪声是有色的，而噪声的模型结构又不好确定时，增广最小二乘法和广义最小二乘法一般都不好直接应用，因为他们需要选用特定的模型结构，而辅助变量法不需要确定噪声的模型结构，因此辅助变量法就显得更为灵活，但辅助变量法不能同时获得噪声模型的参数估计。(Choose appropriate secondary variables, meet the relevant conditions and parameter estimate can be unbiased consistent. Estimated auxiliary variable method calculation and least square method is quite, but the identification effect is much better than the least square method. Especially when the noise is colored, and noise model structure and not sure, augmented the least squares and the generalized least squares method is generally not used directly, because they need to choose specific model structure, and auxiliary variable method does not need to make sure that noise model structure, so the auxiliary variable method is more agile, but not at the same time auxiliary variable method for noise model parameter estimation.)