30045836Fuzzy_K

Analysis of BER in FSK Modulation with AWGN Fading Channel

在Matlab环境中编制了以雨流计数法为理论基础的二维载荷谱程序,用以对混合动力电动车电机驱动系统载荷时间历程进行统计处理,得到载荷幅均值雨流矩阵,进行幅均值各自分布假设检验,以及独立性和相关性检验,获得二维联合分布概率密度函数,建立混合动力电动车电机驱动系统台架试验用二维载荷谱.(Prepared in Matlab environment of the rain flow counting method with the theoretical basis of the two-dimensional load spectrum procedure for hybrid electric vehicles on the motor drive system for statistical processing time course load, get a load amplitude mean rain flow matrix, be the mean amplitude of each distribution hypothesis testing, as well as independence and correlation test to obtain a two-dimensional probability density function of the joint distribution, the establishment of a hybrid electric vehicle motor drive system with two-dimensional bench load spectrum.)

matlab实现的1范数优化代码，实现绝对值偏差最小的算法。(matlab norm to achieve a optimized code to achieve the smallest absolute deviation algorithm.)

KL展开随机场的程序，利用伽辽金法计算，几个类型的相关函数(The program of KL expansion random field is calculated by Galerkin method)

求高斯混合模型的EM算法，matlab程序。(Seeking EM algorithm for Gaussian mixture model, matlab program.)

说明：  屎王nes资料，屎王nes资料，关于任天堂主机的(nes infomation，nes infomationnes infomation)

PV MODEL USEFUL FOR MATLAB SIMULATION

说明：  合成孔径雷达系统MATLALB模拟实现源码。(Synthetic aperture radar system simulation MATLALB realize source.)

Linear regression with regularization

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)