GalileocodinginRaily

Simple chemical kinetics example with 3 model parameters.

说明：  一个小程序，matlab_gui编程，关于主成分分析，界面和谐，参考价值高。(A small program, matlab_gui programming, on the principal component analysis, interface harmony, the high reference value.)

用内点法实现电力系统潮流的优化问题上，代码完整，已经通过测试(Interior point method for power system optimization)

有限元SpaceFrameElement的matlab程序(FEA matlab SpaceFrameElement)

Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course in numerical methods, Matlab, and technical computing. The emphasis is on in- formed use of mathematical software. We want you learn enough about the mathe- matical functions in Matlab that you will be able to use them correctly, appreciate their limitations, and modify them when necessary to suit your own needs. The topics include * introduction to Matlab, * linear equations, * interpolation, * zero and roots, * least squares, * quadrature, * ordinary di?erential equations, * random numbers, * Fourier analysis, * eigenvalues and singular values, * partial di?erential equations.(Numerical Computing with MATLAB (by Cleve Moler) is a textbook for an introductory coursein numerical methods, Matlab, and technical computing. The emphasis is on in-formed use of mathematical software. We want you learn enough about the mathe-matical functions in Matlab that you will be able to use them correctly, appreciatetheir limitations, and modify them when necessary to suit your own needs. Thetopics include* introduction to Matlab,* linear equations,* interpolation,* zero and roots,* least squares,* quadrature,* ordinary di? erential equations,* random numbers,* Fourier analysis,* eigenvalues and singular values,* partial di? erential equations.)

ML-KNN实现过程，包括训练和测试两个过程(the process of ML-KNN, including training and testing )

solo es u programa dejen de moler

关于定位技术的一个最新的Tdoa方面的matlab代码(Positioning technology an up-to-date the TDOA aspects of matlab code)

Facial Recognition program kNN

In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via Support Vectors (SVDA), is introduced by using the SVM. The kernel mapping idea is used to derive the non-linear version, Kernel Discriminant via Support Vectors (SVKD). In SVDA, only support vectors are involved to obtain the transformation matrix. Thus, the computational complexity can be greatly reduced for kernel based feature extraction. Experiments carried out on several standard databases show a clear improvement on LDA-based recognition