upf_demos

布谷鸟搜索（Cuckoo Search，缩写 CS），也叫杜鹃搜索，是由剑桥大学杨新社（音译自：Xin-She Yang）教授和S.戴布（S.Deb）于2009年提出的一种新兴启发算法[1]。 CS算法是通过模拟某些种属布谷鸟的寄生育雏（Brood Parasitism） [2]，来有效地求解最优化问题的算法[3]。同时，CS也采用相关的Levy飞行搜索机制。研究表明，布谷鸟搜索比其他群体优化算法更有效(Cuckoo search (Cuckoo Search, acronym CS), also known as cuckoo search by Cambridge Young Novosti (transliteration since: Xin-She Yang) inspired a new professor and S. Dai cloth (S.Deb) proposed in 2009 algorithm [1]. CS algorithms by simulating certain species of parasitic cuckoo brood (Brood Parasitism) [2], to efficiently solving algorithm optimization problems [3]. Meanwhile, CS also uses related Levy flight search mechanism. Studies have shown that, compared to other groups cuckoo search optimization algorithm is more efficient)

基于carsim的ABS simulink模型，采用PID控制，具有自动道路识别功能。

VC++与MATLAB混合编程及其应用 摘 要 本文详述了在VC++6.0编译环境下如何利用Matlab来实现应用系统的设计和分析，介绍了通过Matlab与VC应用程序连接的常用方法，分析了VC与Matlab的混合编程的步骤，最后通过一个具体实例的实现过程对上述方法进行了应用性介绍。 ()

用于人脸识别matlab程序 有图片可以测试(Procedure for face recognition matlab image can test)

模拟数字基带系统在matlab下的实现，包括输入输出信号的码型，功率谱密度，眼图及误码率(Analog-to-digital base-band system in the realization of matlab, including the code input and output signal type, the power spectral density, eye diagram and bit error rate)

Implementation of Ceaser Cipher in C

说明：  上传内容为A*算法用于路径规划的完整算法，便于大家学习进步。(The upload content is the complete algorithm of a * algorithm for path planning, which is convenient for everyone to learn and progress.)

This MATLAB code demonstrates the finite-difference frequency-domainmethod by modeling transmission and reflectance from a photonic crystal.(This MATLAB code demonstrates the finite-difference frequency-domain method by modeling transmission and reflectance from a photonic crystal.)

精通MATLAB－综合辅导与指南（附例程）(Proficient in MATLAB-integrated counseling and guidance (attached routines))

Feature selection methods for machine learning algorithms such as SVR, including one filter-based method (CFS) and two wrapper-based methods (GA and PSO). The gridsearch is for the grid search for the optimal hyperparemeters of SVR. The SVM_CV is for the k-fold cross-validation of SVR. All the programs are flexible and could be implemented by the users themselves.