MATLAB

functions for data representation

通过matlab仿真对多种不同的盲均衡算法收敛性能进行了比较(By matlab simulation for a variety of different blind equalization algorithm compares the performance of convergence)

支持向量机matlab程序 (1) Main_SVC_C.m --- C_SVC二类分类算法 (2) Main_SVC_Nu.m --- Nu_SVC二类分类算法 (3) Main_SVM_One_Class.m --- One-Class支持向量机 (4) Main_SVR_Epsilon.m --- Epsilon_SVR回归算法 (5) Main_SVR_Nu.m --- Nu_SVR回归算法(Support vector machine matlab procedures (1) Main_SVC_C.m--- C_SVC II classification algorithm (2) Main_SVC_Nu.m--- Nu_SVC II classification algorithm (3) Main_SVM_One_Class.m--- One-Class Support Vector Machine (4 ) Main_SVR_Epsilon.m--- Epsilon_SVR regression algorithm (5) Main_SVR_Nu.m--- Nu_SVR regression algorithm)

polar plot for signal loss

Fast algorithm for computing the response of a discrete Volterra model given a input sequence.

使用预测控制中的MPC控制算法，利用MATLAB进行编程，对传递函数进行控制。(Use predictive control MPC control algorithm, using MATLAB programming for the transfer function control.)

好的合成孔径雷达的算法程序，不需要要进行复杂的差值运算sar cs(sar cs)

把辨识分成两步进行：第一步：利用相关分析法获得对象的非参数模型（脉冲响应或相关函数）；第二步：利用最小二乘法、辅助变量法或增广最小二乘法等，进一步求的对象的参数模型。如果模型噪声与输入无关，则Cor-ls相关最小二乘法（二步法）可以得到较好的辨识结果。Cor-ls相关最小二乘法（二步法）实质上是先对数据进行一次相关分析，滤除了有色噪声的影响，再利用最小二乘法必然就会改善辨识结果。能适应较宽广的噪声范围，计算量不大，初始值对辨识结果影响较小。但要求输入信号与噪声不相关(The identification is divided into two steps: the first step: using the correlation analysis method for object nonparametric model (impulse response or related function) The second step: using the least squares method, auxiliary variable method or augmented least squares method and so on, further for the parameters of the object model. If the model input noise and independent, the Cor- ls related least squares (two footwork) can get good recognition results. Cor- ls related least squares (two footwork) is essentially the data to a correlation analysis, filter in addition to the influence of colored noise, using least squares inevitable will improve the identification result. To adapt to a wide range of noise and calculation is not big, the initial value to identification results less effect. But for the input signal and noise are not related)

一维边值问题线性有限元数值解法的matlab程序。有详尽的算法分析，程序注释清楚。适用于有限元方法的初学者，能帮助读者加深对基本概念的理解。(one-dimensional linear boundary value problems finite element numerical solution of Matlab procedures. A detailed analysis of algorithms, clear procedures for the Notes. Application of the finite element method in the beginners will help readers to better understand the basic concepts.)

optimal quantizer for several common used distribution, and very fast running speed(optimal quantizer for several common used distribution,and very fast running speed)