MATLABxinhaochuli

这是用牛顿法做的，绝对正确无误，请放心下载。(This is done using Newton s method is definitely correct, please rest assured that download.)

matlab 主成分分析法程序，以目标威胁度评估为例(matlab principal component analysis procedures in order to target the threat assessment as an example)

随机森林的matlab版本，重修改整理过的，可以直接拿来用，带实例。可以用来做回归法分析及分类。是机器学习中的快速有效的方法，基本不会产生过拟合。(Random Forest, an efficient regression and classification tool in area of machine learning. The Programms performs well and a demo is offered.)

model of induction motor(parameters)

采用线性插值的反radon变换，按照原理直接编的，要求tau-p域中的p都是从正数开始的(Code in accordance with the principle of direct linear interpolation of the anti-radon transform, the tau-p domain p from positive)

............... Nonlinear Adaptive Flight Control Using Backsteppin ..................................................................................................................

介绍了matlab工具箱的函数，方便学习和查找，迅速了解相关函数的功能(Introduced matlab toolbox function, easy to learn and find quickly understand the function of the correlation function)

文章介绍了某些结构优化算法，可以给初学者提供参考。(The article describes the structure of the optimization algorithm can give beginners some reference.)

设计卫星绕地球运动的图象。要求：地球的直径≥5cm，卫星用五角星表示，卫星轨迹呈椭圆其中一部分轨迹被地球遮挡。(Design of the satellite around the earth in the image. Requirements: Earth' s diameter ≥ 5cm, with a five-pointed star, said the satellite, the satellite orbit was elliptical orbit by the Earth blocking part.)

This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.