资源列表
LFDA_GMM_MRF_demo
- 高光谱图像分类代码，发表于2016年JSTARS，效果较好-High spectral classification code, the algorithm is published in 2016 TGRS, the results are better
code_CNN_PPF
- 高光谱分类代码，算法发表于2016年TGRS，效果较好-High spectral classification code, the algorithm is published in 2016 TGRS, the results are better
gcforest
- 周志华教授深度森林算法代码，用于分类精度接近深度学习算法-Professor zhihua s deep forest algorithm code is used to classify precision approach to deep learning algorithm
pangyun-V2.5
- 实现六自由度运动学逆解算法，实现了对10个数字音的识别程序利用matlab GUI实现的串口编程例子。- Six degrees of freedom to achieve inverse kinematics algorithm, Realization of 10 digital audio recognition program Use serial programming examples matlab GUI implementation.
PSO-LSSVM-CLASS
- 经过优化得到的参数组，利用优化的参数构建LS-SVM模型，然后使用训练样本对其进行训练。 利用训练后的LS-SVM对测试样本进行分类，-The optimized parameters are used to construct the LS-SVM model with optimized parameters, and then trained using training samples. Using the trained LS-SVM to classify the tes
PSO-LSSVM
- 利用改进PSO算法对LS-SVM进行参数优化，参数 和 的取值范围分别为 和 ，粒子种群数量为 25，迭代次数为 100，惯性权重因子 和 取0.9和0.1，学习因子 和 均取2。-The parameters of PS-SVM are optimized by using the improved PSO algorithm. The range of parameters is 25, the number of particles is 25, the number of iterati
basicofARA
- 经典的人工雨滴算法的流程图，描述了算法的流程- U7ECF u7104 u7L09 u7R4 u7R09 u7R4 u6E1
Sutapa_INF
- 人工智能领域的自然启发式优化算法，来源于自然界降雨启发- U4EBA u5A5 u667A u808 u986 u986 u7169 u7329 u7R0 U542F u53D1
Soft-Computing-2010
- 人工智能领域的自然启发式优化算法，来源于大自然降雨的启发- U4EBA u5D3 u06F0 u03F0 u3169 u7329 u7R0 U7684 u542F u53D1
ACATSP
- 蚁群算法是群智能算法的一种，用来寻求极值问题和解决路径优化问题。有自己编的也有老师给的，可供初学者参考。-Ant colony algorithm is a kind of swarm intelligence algorithm, which is used to seek the problem of extremum problem and solve path optimization problem.
season
- 温特线性和季节性指数平滑算法适用于需求增长且伴有季节性特点的预测-The linear and seasonal index smoothing algorithm is suitable for the prediction of demand growth and seasonal characteristics
average-moving
- 平均移动算法适用于近期影响较大，需求比较稳定的预测。-The average moving algorithm is suitable for the prediction of the recent impact largely and the stability of demand.