31-35

宽带波束形成，有点小问题，欢迎大家批评指正(broadband beamforming, a little, and welcomes your comments and corrections)

直接序列扩频通信系统误码率分析,主要过程为基带信号生成，扩频，解扩，误码率分析(Direct sequence spread spectrum communication system BER analysis, the main process for the baseband signal generation, spreading, despreading, BER analysis)

计算CN，即共同邻居的代码，用于链路预测，属于根据网络的局部特征进行预测(Calculating CN, i.e. common neighbors code for link prediction, according to the local characteristics of the network belonging to predict)

说明：  一种新颖的启发式优化算法：飞蛾扑火算法（MFO）(A novel heuristic optimization algorithm: moth flame-fighting algorithm (MFO))

曲线拟合范例 已知y=x^(-1/2),x分别取1:9 通过训练拟合，推测x=10和11时的y值(Known example of curve fitting y = x ^ (-1/2), x respectively 1:9 fit through training, that x = 10 and 11 when the y value)

matlab GUI 示例，利用guide命令。主要是说明了一下框架和用法。(matlab GUI example, the use of guide command. Is mainly explained about the framework and usage.)

matlab的opt直线函数程序。对坐直线运动时的成像质量可以得到opt评价。(Matlab opt linear function procedures. Sit straight movement of the imaging quality can be opt evaluation.)

Ant colony Optimization

also sometimes called PRK, phase reversal keying, or 2PSK) is the simplest form of phase shift keying (PSK). It uses two phases which are separated by 180° and so can also be termed 2-PSK.

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)