GA-TSP

matlab图像融合工具箱,实现多源传感器图像融合功能(Matlab toolbox image fusion, multi-sensor image fusion function)

我下的有关模糊控制在matlab中的应用电子书，非常好(I was under the control of the fuzzy application in matlab book, very good)

MATLAB语言与控制系统仿真ch2本书基于MATLAB 5.2版，提供了使用MATLAB的实践性指导。MATLAB已成为适合多学科、多种工作平台的功能强大、界面友好、语言自然并且开放性强的大型优秀应用软件，同时也已成为国内外高等院校高等数学、数值分析、数字信号处理、自动控制理论以及工程应用等课程的基本教学工具。本书按逻辑编排，自始至终用实例描述；内容完整且每章相对独立；是一本简明的MATLAB参考书，既适用于初学者，也适用于高级MATLAB用户。对MATLAB与FORTRAN、C等语言结合的描述更是所有MATLAB书籍中少有的一大特色。 本书适合作为理工科高等院校研究生、本科生教学用书，也可作为广大科研工程技术人员的自学用书。(MATLAB language and Control System Simulation ch2 book based on MATLAB 5.2 version, to provide practical guidance for using MATLAB. MATLAB has become a suitable the multidisciplinary working platform powerful, user-friendly, natural language open large outstanding application software, as well as domestic and foreign institutions of higher learning higher mathematics, numerical analysis, digital signal processing, automatic basic teaching tools of control theory and engineering applications courses. The book is organized in a logical, throughout the examples described complete and relatively independent of each chapter is a concise MATLAB reference books, both for beginners and for advanced MATLAB users. Combined with the description of MATLAB and FORTRAN, C, and other languages ​ ​ is a rare feature in the books of all MATLAB. This book is suitable as a science and engineering universities graduate, undergraduate teaching books, the majority of scientific engineering and)

使用MATLAB实现的基于多类别的KNN算法，简称为MLKNN(Using MATLAB to achieve KNN algorithm based on multi-category, referred to as MLKNN)

It generates a 2dbreast phantom

zooming & shrinking matlab code

svm源码 支持向量机 文本分类算法(the svm source text classification algorithm Support Vector Machine)

Siggraph上的MIT的CSAIL的运动放大器,可以通过视频读取脉搏(The MIT CSAIL of Siggraph on sports amplifier can be read via video pulse)

灰色神经网络的预测算法—订单需求预测matlab源代码(Gray neural network prediction algorithm- order demand forecast)

用债券数据估计NS和nss的利率期限结构估计(estimation of interest rate term structure using nss and ns model)