actuator1

matlab做的图像检索源码，算法实现不错，很有借鉴作用，对初学者更是适用。(matlab source code to do image retrieval algorithm to achieve good, very useful reference for beginners it is applicable.)

小波去噪的matlab程序。有三种阈值选择方法：标准的，强制的，自己设置的。(Wavelet denoising matlab program. There are three kinds of threshold selection method: standard, mandatory, set up their own.)

近红外光谱提取后，matlab环境下可对NIR光谱数据进行正交信号校正(After the near-infrared spectrum extraction matlab environment can NIR spectral data orthogonal signal correction)

说明：  MATLAB的学习，以及仿真B超的field II 软件以及使用说明书(MATLAB )

动态规划的matlab程序，参看胡良剑等编《数学实验--使用MATLAB》P180，所举的例子，希望对初学者有用(matlab dynamic programming)

信号在采集和输送过程中往往会参杂着噪声和干扰，将自适应神经网络与信号处理结合起来，采用最小均方（LSM）算法设计的线性自适应滤波器将信号中的杂波成分滤除。用matlab进行仿真,证实线性自适应滤波器具有良好的滤波功能。(Signal acquisition and delivery process often mingled with the noise and interference, the adaptive neural network combined with signal processing, with a minimum mean square (LSM) linear adaptive filter algorithm designed to signal noise filter components In addition. Using matlab simulation confirms linear adaptive filter has a good filtering.)

Least Mean Square ALgorithm

卡尔曼跟踪代码 卡尔曼跟踪代码 卡尔曼跟踪代码 卡尔曼跟踪代码 卡尔曼跟踪代码 (Kalman Kalman tracking code tracking code tracking code Kalman Kalman Kalman tracking code tracking code)

说明：  直流微电网故障仿真模型，还不错，控制模块可以参考下。并网方式可学习。(The fault simulation model of the DC microgrid is not bad, and the control module can be referred to. You can learn how to connect to the network.)

一种基于最小熵反褶积( ]8+83)3 6+D*(A4 Q20(+W(-)D8(+，]6Q) 的滚动轴承故障特征提取方法: 在利用 /X 模型去除齿轮啮合产生的确定性信号的基础上，对保留信号进行最小熵反褶积，增强冲击信号"该方法避免了传统轴承故障诊断方法中带通滤波器设计的难题，实车测试表明: 与共振解调技术相比，该方法提取的滚动轴承故障特征更加明显，更适合于工程应用"(Based on minimum entropy deconvolution (] 8+83) 3 6+D* (A4 Q20 (+W (-) D8 (+,] 6Q)' s fault feature extraction method: using/X model to remove the uncertainty generated signal gear on the basis of the retention signal minimum entropy deconvolution, and enhance the impact of signal " This method avoids the problems of traditional bearing fault diagnosis method of the bandpass filter design, real vehicle test showed that: compared with the resonance demodulation technology, which fault feature extraction method is more obvious, more suitable for engineering applications. " )