UKF 卡尔曼滤波 目标航迹融合 matlab

The MatClass IV Toolbox for MatLab has been developed to support experiments on feature based classification problems. (The MatClass IV Toolbox for MatLab has been developed to support experimentson feature based classification problems.)

说明：  hi image denoising.......it works like a charm

matlab chapter 2 codes

104规约程序，用于电力IEC104规约分析(104PROTOCAL)

说明：  popfnn神经网络，它是一种模糊伪输出的神经网络(pseudo-outer-production fuzzy nerual network).可用于模式识别。运行pop1可以训练输入的特征向量，extractionpop使用来抽取图片的特征向量的。(popfnn neural network, it is a pseudo-output fuzzy neural network (pseudo-outer-production fuzzy nerual network). can be used for pattern recognition. Can be trained to run pop1 input feature vector, extractionpop used to extract the image feature vector.)

实现了图像处理中的频域低通处理，适合图像处理的初学者。matlab实现。(Image processing to achieve a low-pass frequency domain processing, image processing for beginners. matlab implementation.)

电力系统故障计算，能进行横向故障，纵向故障，同时能计算出各支路的短路电流。(Power system fault calculations, can be transverse faults, vertical fault, and can calculate the short-circuit current of each branch)

使用matlab的simulink工具箱，对平面二连杆机构进行动态仿真(Matlab dynamic simulation of Two-linkage mechanism)

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)

LPC Linear prediction coefficients