solving-equations-

说明：  simulink 讲解了simulink的主要功能，比较详细(simulink)

比较有用的matlab源码，用于数据回归分析等。(Matlab more useful source for data regression analysis.)

我的资料主要是对MATLAB编程中常遇到的一些错误进行了归类分析，绝对实用(My information is mainly classified MATLAB programming often encountered some errors, absolutely practical )

matlab 打开bil格式文件的源代码(The bil format can be opened using Matlab with this program)

因此假设了一种机动目标模型：初始匀速直线阶段、匀速圆周运动阶段、返回匀速直线阶段，在此过程中线速度大小?v?保持不变。利用扩展卡尔曼（extended kalman filter, EKF）滤波算法进行定位跟踪，仿真结果表明，该假设模型既符合机动实际，又便于数学处理，并且滤波算法过程稳定，具有较快的收敛速度和较高的定位精度，提高了机动目标跟踪的精度和系统的实时性。(This paper proposes a maneuver target model: the initial phase of the uniform linear, uniform circular motion stage, returning to uniform linear stage, and the size of the line speed v remaining unchanged in this process. The extended Kalman filter (EKF) was used to track position, and simulation results show that the model is in line with the actual motor and easy to deal with the mathematics. The algorithm is stable and has rapid convergence rate and high positioning accuracy to improve the accuracy of the mobile target tracking and real?time system.)

说明：  一本关于时域有限差分介绍的书籍，含MATLAB开发的程序代码。(An introduction on the finite-difference time-domain books, including the development of MATLAB code.)

Matlab interface for the image segmentation algorithm of Efficient Graph-Based Image Segmentation Pedro F. Felzenszwalb and Daniel P. Huttenlocher International Journal of Computer Vision, 59(2) September 2004.

基于修正的hausdorff的图像匹配算法，可快速实现图像匹配(hausdorff mattch)

使用MAtlab语言，在一幅图像中添加各种噪声，比如：椒盐噪声，高斯噪声，均匀，指数，对数，厄尔等噪声。(Using Matlab language, to add a variety of noise in an image, such as: salt and pepper noise, Gaussian noise, uniform, exponential, logarithmic, El noise.)

利用SVM做回归线性测试。对于大盘指数的有效预测可以从整体上观测股市的变化提供强有力的信息，所以对上证指数的预测很有意义。通过对上证指数从1990.12.20-2009.08.19每日的开盘数进行回归分析，最终拟合的结果是：均方误差MSE=2.35705 e-005，平方相关系数 R=99.9195 。SVM的拟合结果还是比较理想的。(Make use of SVM linear regression testing. For the market index can predict the observed changes in the stock market as a whole to provide a strong information, so it makes sense to predict the Shanghai index. Through the Shanghai Composite Index opened daily from 1990.12.20-2009.08.19 number of regression analysis, the results of the final fit are: mean square error MSE = 2.35705 e-005, the square of the correlation coefficient R = 99.9195 . SVM fitting result is quite satisfactory.)