搜索资源列表
Extremal-Optimization5
- 极值优化论文5：嵌入极值优化的混合粒子群优化算法。嵌入极值优化的混合粒子群优化算法.-Extreme optimization papers 5: embedded global optimization hybrid particle swarm optimization algorithm. Embedded in the the Extremal Optimization hybrid particle swarm optimization algorithm.
likehood
- 针对遮挡、光照变化、尺度变化等复杂环境中的视觉跟踪问题, 提出一种基于后验概率 度量的粒子滤波跟踪算法-For shelter, illumination changes, scale variations in complex environments such as visual tracking problem, a posterior probability metrics based on particle filter tracking algorithm
PSO-LSSVM
- ：针对暖通空调(小，AC)系统，提出一种基于粒子群优化(Pso)算法和最小二乘支持向量机(LSSVM)的预测控 制方法。-: For HVAC (small, AC) system is proposed based on particle swarm optimization (Pso) algorithm and least squares support vector machine (LSSVM) predictive control methods.
cpso1
- 用C++编写的粒子群算法源程序，非常有用，相信会对大家有所帮助-Prepared with C++ PSO source, very useful, we believe it will help
One-kind-of-Kalman-filter
- 一种卡尔曼滤波与粒子滤波相结合的非线性滤波算法 One kind of Kalman filter-One kind of Kalman filter and particle filter combines nonlinear filtering algorithm
NumExamplePF
- 粒子滤波算法的简单例子，适用于非线性、非高斯过程的滤波。-A simple example of particle filter algorithm for nonlinear and non-Gaussian process filtering.
K--B-value-calculation-function
- 测量K,B值，通过空打粒子数，实测粒子数和标准质量厚度计算KB值-K, B value calculation function
object-tracking-
- 基于分块颜色直方图和粒子滤波的物体跟踪_陶立超.zip-Based on block color histogram and particle filter object tracking _ Tao Li Chao. Zip
wave
- 将均值偏移算法嵌入到粒子滤波的跟踪框架中.该算法克服了粒子滤波计算量较大的缺点,同时也克服了均值偏移算法容易陷入局部最大且无法恢复的缺点.实验表明该算法有很好的实时性和鲁棒性.-The mean shift algorithm is embedded into the particle filter tracking framework of the particle filter algorithm overcomes the shortcomings of large amount of c
Chaos-particle-algorithm
- 针对粒子群优化算法稳定性较差和易陷入局部极值的缺点，论文提出了一种新颖的混沌粒子群优化算法。-Particle swarm optimization for the poor stability and ease of getting into local extremum shortcomings, the paper proposes a novel chaotic particle swarm optimization algorithm.
improved-particle-bionics
- 针对标准粒子群算法收敛速度慢和易陷入局部最优的局限性，提出了一种基于仿生学改进的粒子群算法。-For standard PSO slow convergence and local optimum limitations proposed based on improved particle swarm optimization bionics.
particle-filtering-tracking
- 基于粒子滤波和自适应模型的目标跟踪算法,可以有效提高粒子滤波的准确性和稳定性.-Adaptive model based on particle filtering and target tracking algorithm can effectively improve the accuracy and stability of the particle filter.
Particle-Filter-Review
- 以最优Bayesian滤波的求解为起点 ,综述了粒子滤波的发展历程、基本思想、算法的各个基本环节、基本的滤波算法及其收敛性以及算法的多种重要衍变形式 。-Solving an optimal Bayesian filtering as a starting point, an overview of the development process of particle filter, the basic idea of all the basic aspects o
Particle-Algorithm-Status-Trend
- 介绍了粒子滤波目前主要应用领域, 最后对粒子滤波的发展提出了展望。-Describes the particle filter is currently the main application areas, and finally the development of particle filter raised prospects.
Particle-filter-wireless
- 本文对粒子滤波算法的基本原理及其在无线通信中的应用进行综述,重点介绍其中的几种典型应用。-In this paper, the basic principles of particle filter algorithm and its application in wireless communications are reviewed, focusing on a few of the typical application.
liziquansuanfa
- 粒子群算法的简介，实例运用和理解。粒子群算法的简介，实例运用和理解- the relation and descr iption of PSO
The-Multi-user-Detection-paper
- 提出了基于联合智能算法的MIMO-OFDM系统多用户检测算法。联合智能算法结合了粒子群优化算法、遗传算法、模拟退火算法的思想。-Joint intelligent algorithm is proposed based on multi-user detection algorithm of MIMO- OFDM system. Combined intelligent algorithm combining particle swarm optimization algorithm and g
Visual-saliency--tracking
- 针对突变运动下的目标跟踪问题，提出一种基于视觉显著性的粒子滤波跟踪算法． 该算 法将基于视觉注意机制的视觉显著图引入粒子滤波框架中，根据视觉显著图的显著性区域，按 胜者为王和返回抑制机制进行目标检测-Aimingat solvingthetrackingproblemsunder thecircumstancesof abrupt motion，aparticlefilter trackerisproposedbasedonvisual saliencymodel． Thistracke
TSP
- 总结的混合粒子群算法、蚁群算法、遗传算法求TSP（旅行商问题）,值得看一看-Summary hybrid particle swarm optimization, ant colony algorithm, genetic algorithm for TSP (traveling salesman problem), it is worth a look
YC
- 在这个分支中，主要是对标准粒子群算法的惯性因子、收敛因子（约束因子）、“认知”部分的c1，“社会”部分的c2进行变化与调节，希望获得好的效果。-In this branch, mainly to the standard particle swarm algorithm of inertial factor, convergence factor (constraints), "cognitive" part of c1, "social" part of the c2 changes and