beibaoPSO

说明：  CCNA中文笔记第2章 　DoD模型被认为是OSI参考模型的浓缩品,分为4层,从上到下是1.DoD模型的Process/Application层对应OSI参考模型的最高3层 　　2.DoD模型的Host-to-Host层对应OSI参考模型的Transport层 　　3.DoD模型的Internet层对应OSI参考模型的Network层 　　4.DoD模型的Network Access层对应OSI参考模型的最底2层 (CCNA Chinese Notes Chapter 2 DoD model OSI reference model is considered concentrated products, divided into four layers, from top to bottom is 1.DoD Model Process/Application layer of OSI reference model corresponds to the maximum 3-layer model 2.DoD Host-to-Host layer corresponds to OSI reference model Transport Layer 3.DoD model Internet layer corresponds to OSI reference model Network model Network Access Layer 4.DoD OSI reference model layer corresponds to the bottom layer 2)

在ar人脸库上的sparse kernel feature analysis程序，该程序挨个计算kernel空间鉴别向量，最后组成鉴别向量集进行分类(In the ar face database on the sparse kernel feature analysis program, which identified one by one calculation kernel space vector, the final set of vectors to classify the composition of differential)

说明：  垂直发射弹道仿真程序(采用四元数法).matlab编写。(Vertical launch trajectory simulation program (using the quaternion method). Matlab prepared.)

matlab code for cdma

非线性系统模糊逻辑控制（FLC）的matlab仿真程序(Nonlinear systems fuzzy logic control (FLC) matlab simulation program)

图论算法中的求路径和几何作图算法包括 狄杰斯特拉一算法和二算法还有公切线算法和画图还有文件的自动导入算法(In graph theory algorithm for path and the geometric mapping algorithm)

HFSS 耦合器 微带结构 可调参数(HFSS structure of adjustable parameters of microstrip coupler)

控制理论超前滞后函数matlab例程，简单易用，思路清晰，适合控制理论初学者(Control theory lead and lag function matlab routines, easy to use, clear thinking, suitable control theory for beginners)

Amin Koohi的基于MFCC特征参数提取的说话人识别程序，可运行。(Based on MFCC feature parameter extraction of speaker recognition program Amin Koohi.)

matlab编写的多示例多标记分类程序，采用miSVM算法，svm做分类器，其中附有各个调用函数，和作为例子的数据和主程序，可以运行，可以多多示例多标记样本进行分类。(matlab prepared multi-instance multi-label classification procedures, using miSVM algorithm, svm do classifier, which function with each call, and as an example of the data and the main program, you can run, you can sample a lot of examples of multi-label classification.)