Invariant-moments

matlab 基本语句描述，及一些基本内部函数，很有用的，欢迎共享，谢谢(Differential evolution algorithm is very popular in recentyears, it iswidely used in complex optimi- zation questionsbecause of it’s easy implementation and has less controlparameters. Butconventionaldifferen- tial evolution algorithm has a problem ofstagnation thatcan stop the algorithm convergence. )

智能洗衣机的模糊控制法则及隶属函数，可载入模块中进行使用(Fuzzy control rules and membership functions of the smart washing machines, you can load the module using the)

基于哈日日食的角点检测程序，使用ZNCC进行匹配(yet the solar eclipse on the corner detection, the use of matching ZNCC)

SVPWM控制仿真程序，经调试通过，与大家共分享，MATLAB7.1(SVPWM control simulation program by debugging through, and we were sharing, MATLAB7.1)

基于混沌理论构建的信号检测系统具有敏感微弱周期信号和对噪声有强免疫力的特点,用于微弱信号检测领 域优势明显。实验室内研究需要产生包括混沌噪声在内的各种噪声,并叠加微弱周期信号作为微弱信号检测系统的信号 源。介绍一种基于LabVIEW平台并结合Matlab计算模块的混沌噪声发生器,可产生典型的3种混沌噪声:Logistic,Duff2 ing,Lorenz形式。该发生器将LabV IEW易于接口实现和Matlab计算精度高、编程简捷的特点相结合,具有结构清晰、通用 性强、易于实现、使用方便的特点。 (Built based on chaos theory, sensitive signal detection system with weak periodic signal and has a strong immunity to noise characteristics of the area for weak signal detection have obvious advantages. Laboratory studies need to generate, including noise, chaos, including a variety of noise and superimposed weak periodic signal as a weak signal detection system, signal source. Introduction of a platform based on LabVIEW and Matlab calculation module with the chaotic noise generator can produce the typical three kinds of chaotic noise: Logistic, Duff2 ing, Lorenz form. The generator will be LabV IEW easy interface, and Matlab calculation of high accuracy, simple programming features combined with a clear structure and versatility, easy to implement, easy to use features.)

FlOYD 算法 用于tSP计算旅行商 (FlOYD algorithm is used to calculate the traveling salesman tSP)

AdaBoost元算法属于boosting系统融合方法中最流行的一种，说白了就是一种串行训练并且最后加权累加的系统融合方法。 具体的流程是：每一个训练样例都赋予相同的权重，并且权重满足归一化，经过第一个分类器分类之后， 计算第一个分类器的权重alpha值，并且更新每一个训练样例的权重，然后再进行第二个分类器的训练，相同的方法....... 直到错误率为0或者达到指定的训练轮数，其中最后预测的标签计算是各系统*alpha的加权和，然后sign（预测值）。 可以看出，训练流程是串行的，并且训练样例的权重是一直在变化的，分错的样本的权重不断加大，正确的样本的权重不断减小。 AdaBoost元算法是boosting中流行的一种，还有其他的系统融合的方法，比如bagging方法以及随机森林。 对于非均衡样本的处理，一般可以通过欠抽样(undersampling)或者过抽样(oversampling)，欠抽样是削减样本的数目， 过抽样是重复的选取某些样本，最好的方法是两种进行结合的方法。 同时可以通过删除离决策边界比较远的样例。 (AdaBoost boosting systems dollar fusion algorithm is the most popular one, it plainly systems integration approach is a serial train and final weighted cumulative. Specific process is: Each training example is given equal weight, and the weights satisfy normalization, after the first classifiers after Calculating a first classifier weights alpha value for each sample and updates right weight training, and then the second classifier training, the same way ....... 0, or until the specified error rate training rounds, wherein the label is the calculation of the final prediction system* alpha weighted and then sign (predicted value). As can be seen, the training process is serial, and weight training examples is always changing, the right of the wrong sample weight continued to increase, the right to correct sample weight decreasing. AdaBoost algorithm is an element, as well as other methods of boosting popular systems integration, such as bagging and random forest method. For )

the IEEE754 standard

程序设计 MATLAB 程序 设计 基础(Program design based on MATLAB programming)

data analysis of pap smear data reqiuired in analysis of pap smear images