gsave

matlab宝典，编程使用，可以有效提高程度得效率(matlab，improve the code )

灰度图像边缘检测中的prewitt算法的MATLAB仿真实现(MATLAB simulation grayscale image prewitt edge detection algorithm to achieve)

PCH coding 编码仿真 ， matlab 实现(PCH code simulation, matlab implementation)

The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order (The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order)

说明：  经典的k-means程序,包含了很多的数据集，具有几点的聚类程序的描述。以供大家参考。(kmeans)

Alternate CPU1 startup vector for BMIPS4350.

LMS算法的MATLAB实现以及实例，显示输出序列(LMS algorithm and example MATLAB implementation, show that the output sequence)

An LMS-Based Adaptive Generalized Sidelobe Canceller with Decision Feedback

ics 的malloc实现，采用分离存储，模拟memory的malloc和free等操作。(to realize the molloc and free operation, the lab of ics class)

Matlab Codes for Series Resenance