PSOGSA

基于FFT的自相关函数快速计算方法估计出自相关函数(Fast calculation method based on the FFT of the autocorrelation function is estimated from the correlation function)

本书把MATLAB 当作一种技术编程语言来教，向学生展示如何编写简洁高效，有充分 根据的程序。本书不主张把MATLAB 数以百计的函数全部介绍给大家，而是教给学生怎样 使用MATLAB 语言，以及如何使用网上在线工具去加载你所需要的函数。本书的前六章是 “编程设计介绍”的课程，专门为大一的工科学生而设计编写的。这部分内容以9 周，每周 3 个小时教学为宜。其余的章节涵盖了一些高级的主题，例如输入输出接口（input/output interfaces）和图形用户界面（GUI）。这些章节可能需要更长时间的学习，也可以作为使用MATLAB 的工科学生和有经验的工程师的参考。(The book as a technique to MATLAB programming language to teach, to show students how to write a simple and efficient, there is a sufficient basis for the program. This book does not advocate a function of all the hundreds of MATLAB to introduce to you, but to teach students how to use MATLAB language, and how to use on-line tools to load the function you need. The first six chapters of this book is " designed to introduce programming" course, designed for freshman engineering students designed to write. This part to nine weeks, three hours a week teaching appropriate. The remaining chapters cover some advanced topics, such as input and output interfaces (input/output interfaces) and graphical user interface (GUI). These chapters may take longer to learn, you can also use MATLAB as a reference for engineering students and experienced engineers.)

遗传算法和最速下降法的比较及其各自的优缺点(Genetic algorithms and compare the steepest descent method and their respective advantages and disadvantages)

利用matlab实现绘图文档Drawing documents using matlab implementation(Using matlab to achieve drawing document Drawing documents using matlab implementation)

势阱中的波函数,matlab程序,彭芳鳞教材(The wave function in the potential well)

Lilliefore test matlab代码(matlab code for Lilliefore test)

粒子群和自适应混沌粒子群算法的程序，作了简单对比(Chaos particle swarm optimization and adaptive particle swarm optimization program, made ​ ​ a simple comparison)

说明：  四自由度船舶运动模型,利用该控制器船舶能够快速、精确地跟踪期望直线和曲线 航迹， 控制器具有较强的鲁棒性(four degrees freedom ship motion)

无迹卡尔曼滤波器(Unscented Kalman Filtering)的MATLAB实现(ukf(Unscented Kalman Filtering)MATLAB implementation)

用SVM进行分类时，需要对原数据进行归一化处理。(When using SVM classification, the need for raw data normalized.)