资源列表
A_star
- 能够进行路径规划，用于全局路径规划，是目前较为广泛的全局路径规划算法(Can perform path planning for global path planning. It is currently a relatively extensive global path planning algorithm.)
Genetic algorithm optimization calculation
- 遗传算法的优化计算—输入自变量降维，利用遗传算法对BP神经网络进行优化(Optimization calculation of genetic algorithm -dimensionality reduction of input variables ,Optimization of BP neural network by genetic algorithm)
kmeans-algorithm
- 一个基础kmeans算法，内含有数据文件，可以直接执行。(A basic kmeans algorithm, which contains data files, can be directly executed.)
GudongRecommendation-master
- 基于深度学习和协同过滤算法实现问卷调查内容推荐，通过深度学习中的tensflow构建项目评分矩阵，利用协同过滤算法产生推荐结果。(Based on the depth learning and collaborative filtering algorithm, the content of the questionnaire is recommended. The project score matrix is constructed through the tensflow in depth
JianKeWeb-master
- 基于SPEA2和MOGA算法实现的多目标优化，主要针对营养平衡目标进行规划。(The multi-objective optimization based on SPEA2 and MOGA algorithm is mainly aimed at planning the goal of nutrition balance.)
Dropout
- Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification 2016年CVPR的一篇论文 行人再识别方法(Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification A CVPR Paper 2016 Pedestrian Reidenti
HOG 代码.docx
- HOG 方向梯度直方图（Histogram of Oriented Gradient, HOG）特征是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子(The Histogram of Oriented Gradient (HOG) feature is a feature descr iptor used for object detection in computer vision and image processing.)
LSTM-MATLAB-master
- 亲测有效，效果很好~~感觉写的很简明，有注释(Pro-test effective, good effect, feeling very concise with comments)
DPM
- 行人检测的很好，现在很多数据库都用DPM做这个检测(Pedestrian detection is very good. Now many databases use DPM to do this detection.)
motor_svpwm
- 感应电机开环SVPWM仿真完整数据模型，调试通过，直接可用。(Simulation of SVPWM based AC MOTOR)
CS_SVM_exmp
- 该程序是cs-svm的程序，用于对svm的算法优化(This program is a program of cs-svm, which is used to optimize the algorithm of SVM.)
PSO_ELM
- 运用粒子群算法对ELM算法进行优化，以达到算法的最优性。(Particle swarm optimization (PSO) is applied to optimize the ELM algorithm to achieve the optimality of the algorithm.)