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1、基本要求： (1)频率测量 测量范围：1HZ~1MHZ，信号为方波、正弦波；幅度为0.1V~5V； 测量误差： 0.1 （以NFC—10A为准）。 (2)周期测量 测量范围：1HZ~1MHZ，信号为方波、正弦波；幅度为0.1V~5V； 测量误差：， 0.1 （以NFC—10A为准）。 (3)脉冲宽度测量： 测量范围：脉冲宽度不超过100μS，信号为脉冲波，幅度为0.1V~5V； 测量误差： 0.1 。 (failed to translate)

一个三维的有限元程序代码， 程序里有详细的注释，包括网格的剖分等 参考文献，输入文档。(A three-dimensional finite element program code, program, there are detailed notes, including grid mesh and other references, the input document.)

QR 分解 （包括Householer, Givens, 经典Gram Schmidt, 修正的GS methods)(QR factorization including Householer, Givens, CGS, MGS)

一个单一的数字控制，非常简单的移动。MATLAB常用电路的设计方法(A single digital control, very simple mobile.Matlab common circuit design method)

自己写的关于信噪比最大化的信号盲分离算法例子，希望对大家有用(Write about the signal to noise ratio maximization algorithm for blind signal separation example, we hope to be useful)

MATLAB5.3 exciting programming and advanced applications

可以实现粒子群算法与盲源分离算法的结合，比较方便。源信号可以随便替换(PSO algorithm can be achieved with the combination of blind source separation algorithms, more convenient. Source signal can be easily replaced)

支持向量机和BP神经网络虽然都可以用来做非线性回归，但它们所基于的理论基础不同，回归的机理也不相同。支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。为了验证这种观点，本文编写了支持向量机非线性回归的通用Matlab程序和基于神经网络工具箱的BP神经网络仿真模块，仿真结果证实，支持向量机做非线性回归不仅泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。 (Support Vector Machine and BP neural network, even though there can be used to make non-linear regression, but they are based on the theoretical basis for the different, the mechanism of regression is not the same. Support vector machine based on structural risk minimization theory, generally considered the generalization ability of neural networks than the strong. To test this view, the paper prepared by non-linear regression support vector machine procedures and based on a common Matlab neural network toolbox of BP neural network )

pwm控制模块，可以仿真太阳能电池的采样点输入(PWM control module, can simulate the solar cell sampling input)

采用二阶盲源分离程序编写的模态参数识别Matlab工具箱。(Toolbox for performing modal dentification using second order blind source separation methods)