资源列表
MMASForTSP
- 最大最小蚁群算法求解TSP。最大最小蚁群算法通过限制信息素的上下限，使得搜索的开发探索能力更强。-Max- min ant colony algorithm for solving TSP. Maximum and minimum ant colony algorithm pheromone upper and lower limits, allowing developers to explore the ability to search for more.
dynamic-path-planning
- (不错的一篇文章，已被EI收录)模型预测控制（model predictive control，MPC）路径规划算法适用于三维动态环境下的无人机（un-manned aerial vehicle，UAV）路径规划；动态贝叶斯网络（dynamic Bayesian network，DBN）能够有效推理战场态势，对无人机进行威胁评估。针对威胁尾随无人机时的路径规划问题，构建 DBN 威胁评估模型，将 UAV 在战场环境中的威胁态势用威胁等级概率表示，与 MPC 路径规划算法相结合，得到基于 DBN
Improved-Ant-Colony-Optimization
- 将改进的蚁群算法与路径几何优化相结合,用于解决移动机器人的全局路径规划问题.算法结合机器人的越障性能对移动机器人的环境空间进行建模.通过设置初始信息素加快蚂蚁的搜索速度,同时设置自适应信息素挥发机制,解决特定地图中初始信息素的干扰问题 设置自适应路径长度,筛选规划路径的优劣 提出由路径优劣程度决定的信息素散播策略,并从几何原理出发,对规划路径进行优化处理,加快最优解的收敛速度.仿真结果验证了该算法的有效性和普遍应用性,在随机给定的环境地图中,该算法能够迅速规划出最优路径.-The improve
clusteringAnalysis
- 模式识别中K均值聚类分析算法的matlab实现及注释，其中采用了误差平方和判断。-K-means clustering analysis algorithm in pattern recognition of matlab and annotation, which adopts the error sum of squares of judgment
NSGA-II2
- 基于非支配选择的遗传算法（NSGA-II），可用于求解多目标问题，并给出给定数目的帕累托前沿-Based on the non-dominant selection genetic algorithm (NSGA-II), can be used to solve the multi-objective questions and a given number of Pareto frontier
proj1_knn
- MATLAB实现的KNN 压缩文件包含m文件和wine.txt(机器学习著名的红酒数据集，下载自UCI大学机器学习数据集： http://archive.ics.uci.edu/ml/datasets/Wine） -knn wrote in MATLAB
6-axis-motor-algorithm
- 六轴机器人正运动学、逆运动学求解的原创性资料，作者经验之总结比书上的资料更有参考价值。-Six-axis robot forward kinematics, inverse kinematics originality data, the authors summarize the experience of the information in the book is more than a reference value.
NSGA-II_20140417
- 遗传算法多目标优化的一个版本，内容包括源程序和NSGA-II工程包nsga2code使用手册- A version of the genetic algorithm for multi-objective optimization, including source code and NSGA-II project package nsga2code Manual
cnn_cuda5.5
- 利用cuda加速卷积神经网络，用于人工智能图像分类- Cuda accelerated use convolution neural networks, artificial intelligence for image classification
fast-rcnn-master
- Fast Region-based Convolutional Networks for object detection. Fast R-CNN** is a fast framework for object detection with deep ConvNets. Fast R-CNN - trains state-of-the-art models, like VGG16, 9x faster than traditional R-CNN and 3x faster than
fuzzy-control
- 模糊控制实现温区的恒温控制,里面给出的是一个例子-Fuzzy control thermostatically controlled temperature zones, which gives an example
SAPSO
- 对粒子群算法的改进，利用一种名为自适应权重粒子的方法来更新粒子速度和位置，用来解决静态单目标优化问题-It is the improved particle swarm optimization algorithm. It introduced a kind method named adaptive weight to update the position and velocity of the particle. It can be used for handling single obj