huiseyuce

Outlier Detection and Prediction Using Solving Set

滚仰式导引头的解耦控制。程序可以实现消除弹体运动的干扰，实现导引头的实时跟踪(seeker decoupling)

粒子群算法(pso)标准测试函数验证程序。在一个m文件中包括了目前文献中用于验证的7个标准测试函数(Ackley等)、三维动态显示，粒子过分集中时打散等功能。旨在为学习和研究者pso算法的同仁提供一个功能较为完备、简单易懂的标准版本，对于初学者可以通过此程序快速的实现入门，以便将更多的精力投入到深层次的研究中去！同时愿与所有致力于此的朋友共同探讨pso算法的改进与应用方面(如多目标、动态系统等)的经验。(验。-PSO algorithm (PSO) standard test function verification process. M in a document, including the current literature is used to validate the seven standard test function (Ackley), 3D dynamic display, and the over-concentration of particles scatters when the function. Designed for researchers to study and colleagues at the PSO algorithm to provide a more complete functional and easily understood standard version, for beginners through this program to achieve rapid entry. in order to be more energy into depth study! While willing to work with all the friends here to discuss PSO algorithm improvements and applications (such as multi-target, Dynamic systems, etc.) experience.)

基于MATLAB,各种信道的估计方法，共13种(Estimation_of_Kinds_of_Channels(MATLAB))

简单的语法缝隙器，可以比较详尽的分析输入语句的语法成分。把想要分析的语句放到parser.in文件中，编译即可看到结果。(Simple syntax gap, you can enter a more detailed analysis of the syntax of the statement components. The statement you want to analyze into parser.in file, the compiler can see the results.)

说明粒子滤波器的功能，给了5个例题和他的源代码。(Explain the function of particle filter)

解决M/M/1排队论问题，服务时间服从指数分布，队列为泊松流。系统输出为每个个体的到达时间，离开时间，等待时间，逗留时间(M/M/1 queuing theory to solve the problem, the service time is exponentially distributed, the queue is Poisson. System output for each individual arrival time, departure time, waiting time, length of stay)

灰度图像的二维Otsu自动阈值分割法 matlab源程序(Two-dimensional gray image Otsu automatic thresholding method)

进行OFDM系统的matlab仿真，其中采用的子载波数为8个。并且仿真了多普勒频移对OFDM的影响。基带调制采用QPSK(Matlab simulation for OFDM system, which USES the subcarrier number is eight.And simulation of the doppler shift effect on OFDM.Baseband using QPSK modulation)

Four crucial issues are considered by the proposed HoAL: 1) unlike binary cases, the selection granularity for multilabel active learning need to be fined from example to examplelabel pair 2) different labels are seldom independent, and label correlations provide critical information for efficient learning 3) in addition to pair-wise label correlations, high-order label correlations are also informative for multilabel active learning and 4) since the number of label combinations increases exponentially with respect to the number of labels, an efficient mining method is required to discover informative label correlations