curvaCP

说明：  给出了一个控制系统的模型，此模型采用ＭＡＴＬＡＢ模块搭建，解决跟踪问题(Gives a control system model, this model structures using MATLAB module to address the tracking problem)

This file forms an estimate of the frequency spectrum using Pisarenko harmonic decomposition.

说明：  生物特征识别技术作为一种身份鉴别手段具有独特的优势。在生物特征识别系统中, 采集数据的预处 理是不可或缺的环节。以人脸识别为例, 介绍了一种在复杂背景的彩色图像中, 基于颜色和知识的生物特征预处理 方法。首先利用颜色信息从图像中分割出类肤色区域, 然后运用人脸几何特征和眼睛的提取定位人脸。仿真实验表 明, 该算法能较为准确地定位彩色图像中的正面或小角度偏转的人脸, 具有较快的运算速度。(Biometrics as an identification tool has a unique advantage. In biometric systems, collect data preprocessing is an indispensable link. To face recognition, for example, presents a complex background in color images based on color and knowledge of the biological characteristics of pretreatment. The first to use color information to separate out the classes from the image color region, and then use face extraction of geometrical features and eye positioning face. Simulation results show that the algorithm can be more accurately locate color images in a positive or small-angle deflection of the face, with a faster computational speed.)

改进神经网络PID程序用来位置跟踪，比普通的PID位置跟踪稳定性高(PID neural network program is used to improve location tracking, location tracking than conventional PID stability)

基于中值滤波器的红外弱小目标检测，以9*9为滤波窗口大小(Median filter-based infrared small target detection, to 9* 9 for the filtering window size)

信道编码（线性分组码 卷积码），交织器的仿真(Simulation additive white Gaussian noise channel and multi-channel fading by)

遗传算法解决背包问题。内含genetic工具包。(Genetic algorithm to solve knapsack problem. Contains genetic toolkit.)

radau伪谱法解算最优控制，无须安装第三方优化工具箱(RPS optimal control matlab toolbox)

拟牛顿法和最速下降法(Steepest Descent Methods)一样只要求每一步迭代时知道目标函数的梯度。通过测量梯度的变化，构造一个目标函数的模型使之足以产生超线性收敛性。这类方法大大优于最速下降法，尤其对于困难的问题。另外，因为拟牛顿法不需要二阶导数的信息，所以有时比牛顿法(Newton s Method)更为有效。如今，优化软件中包含了大量的拟牛顿算法用来解决无约束，约束，和大规模的优化问题。(The quasi-Newton method and the Steepest Descent Methods only require that each step iterations know the gradient of the objective function. By measuring the change of the gradient, constructing a model of the objective function is sufficient to produce superlinear convergence. This method is much better than the steepest descent method, especially for difficult problems. In addition, because the quasi-Newton method does not require information on the second derivative, it is sometimes more effective than the Newton s Method. Today, the optimization software contains a large number of quasi-Newton algorithm used to solve the unconstrained, constraint, and large-scale optimization problems.)

本程序峭度的计算程序，已做成工具包，可以直接调用(The program kurtosis calculation program, has made kit, you can call)