DIPUMtoolboxV1.1.3

说明：  文件为MATLAB低通滤波器文件，运行文件课得滤波器特性曲线，并且还有实例。(MATLAB files for the low-pass filter documents, running a filter file class characteristic curve, and there are examples.)

matlab - image processing - gaussian high pass filter(matlab- image processing- gaussian high pass filter)

说明：  任何一个检测的信号都不可避免地受到干扰源的影响，脑电检测中的干扰源主要分为体内体外两大类。 体外干扰包括检测系统本身（如电极与头皮的接触阻抗的变化）、交流电源和环境中的电磁干扰源。把脑电图机的接地端与地线向量，并保持电极与头皮的良好接触，可以有效地抑制工频干扰。在电磁屏蔽室中进行脑电采集，能有效地避免环境中的电磁干扰。(eeg eog)

共生矩阵的实现及特征提取方法，里面包含了各个特征向量提取的子程序，可以在其他程序中直接调用。(co-occurrence matrix and the realization of feature extraction methods, each of which contains a feature vector extraction subroutine, the other procedures can be called directly.)

很多通信系统的matlab程序,便于修改调用，欢迎使用！(many communications systems Matlab procedures to facilitate changes called Welcome!)

四维状态、二维观测的目标跟踪扩展卡尔曼滤波程序，附有详细的说明,便于新手学习。(The four-state, two-dimensional observation of the target tracking extended Kalman filtering procedures, with detailed instructions, easy for novices to learn.)

自适应控制经典的课件，国外著名大学的内部学习资料，请不要错过(Adaptive control classic courseware, internal learning famous universities, do not miss)

和先前传的程序是配套的，二者对比着看，有好处，可以更深的理解和应用-Chuan previous procedures are complementary, the two contrast look forward to, good, can be a deeper understanding and application of

说明：  matlab编写的[数据挖掘]分类算法Discrete_Bayes算法程序。(Matlab prepared by the [data mining] classification algorithm Discrete_Bayes algorithm way Preface.)

该算法用于自回归输入模型，是一种迭代的算法。其基本思想是基于对数据先进行一次滤波处理，后利用普通最小二乘法对滤波后的数据进行辨识，进而获得无偏一致估计。但是当过程的输出信噪比比较大或模型参数较多时，这种数据白色化处理的可靠性就会下降，辨识结果往往会是有偏估计。数据要充分多，否则辨识精度下降。模型阶次不宜过高。初始值对辨识结果有较大影响。(The algorithm used for autoregressive input model, it is a kind of iterative algorithm. The basic idea is based on data to conduct a filtering processing, after using ordinary least square method to identify the data filter, and then obtain unbiased consensus estimates. But when the process output signal-to-noise ratio is larger or model parameters are too, this kind of data white processs reliability will drop, identification results tend to be biased estimate. Data should fully, otherwise the identification accuracy down. Model order time shoulds not be too high. Initial value to identification results have great influence on.)