PML

基于最简单的直接转矩控制的matlab程序框图，经试验，完全可以正常运转(Based on the most simple and direct torque control matlab block diagram, the test, can operate normally)

GN25L98 GPON 的芯片手册 详细记载了芯片的使用方法(GN25L98 GPON chip manual Detailed the use of the chip)

说明：  例程，用于功能测试。如程序有错请大家指出.(Routine for function testing. If there is any mistake in the procedure, please point out)

该文件运用matlab仿真工具仿真通信中的关键技术之一，锁相环。采用不同的调制方式。(The document the use of simulation tools for communication matlab simulation of one of the key technologies, phase-locked loop. Different modulation.)

MATLAB信号处理宝典,包括各种函数，滤波器的源程序等(Collection MATLAB signal processing, including the various functions, such as the filter source)

说明：  maltab 图像处理中对ed算法的增强算法(maltab image processing algorithms in the enhancement algorithm on the ed)

仿真对象如下： 其中， v( k )为服从N (0,1) 分布的白噪声。输入信号u ( k) 采用M 序列，幅度为 1。M 序列由 9 级移位寄存器产生，x(i)=x(i-4)⊕x(i-9)。 选择如下辨识模型： 加权阵取Λ = I。 衰减因子β = 0.98，数据长度 L = 402。 辨识结果与理论值比较，基本相同。辨识结果可信 (he simulation object is as follows: among them, v (k) to obey N (0, 1) distribution of white noise. The input signal u (k) using M sequence, amplitude is 1. M sequence by 9 level shift register generation, x (I) = x (I- 4) ⊕ x (I- 9). Choose the identification model: weighted array take Λ = I. Attenuation factor β = 0.98, the data length L = 402. Identify the theoretical calculation and comparison, basically the same. Identification results are reliable)

横向稳定杆的设计校核,在matlab 平台下编写的，已经经过有限元分析验证(antiroll design Horizontal stabilizer bar design check, in matlab platform, has written by finite element analysis and testing )

一种智能优化算法-模拟退火算法的matlab源程序(simulated annealing)

支持向量机是用于分类与回归的技术。由于其自身的诸多缺点，如无法获得概率输出，需要估计一个误 差参数C，以及必须使用Mercer 核函数等。相关向量机算法，克服了SVM 上述缺点，RVM 能获得与SVM 相比拟的推 广性能，并且更为稀疏。在此基础上，文中介绍了一种RVM 回归用于分类的新分类方法，用RVRC 来表示。并通过 实验证明了它的可行性。(Support vector machines for classification and regression techniques. Due to its many shortcomings, such as the probability that the output can not be obtained, need to estimate an error parameter C, and the need to use Mercer kernel function and so on. Relevance vector machine algorithm to overcome these shortcomings of SVM, RVM and SVM can be obtained comparable to promote performance and more sparse. On this basis, the paper introduces a RVM regression was used to classify new classification method, RVRC to represent. And through experiments proved its feasibility.)