资源列表
ant
- 本代码用蚁群算法求解带时间窗的车辆路径问题-The code with ant colony algorithm with time window of vehicle routing problem
JAVA
- 基于JAVA实现的图像特征提取源代码 图像特征提取的源代码，使用sobel算子提取图像边缘，使用hu矩提取图像形状特征，使用颜色中心矩提取图像颜色特征，请使用eclipes导入该工程并运行test文件（可将test文件中去掉一些注释部分运行更多功能）。-JAVA based on the realization of the image feature extraction image feature extraction source code source code, use the sob
RBF
- 基于LM训练算法的自生长RBF神经网络模型.-LM training algorithm based on the RBF neural network model
Prediction_RBF
- matlab编写的基于混沌时间序列的神经网络预测，包括一步和多步预测算法。-matlab prepared chaotic time series based on the neural network to predict, including step and multi-step prediction algorithm.
C_TraDaBoost
- 迁移学习目前是机器学习的热门领域，基于戴文渊的“Boosting for Transfer Learning”,很有学习价值，这里是TraDaBoost算法的源代码。-For transfer learning packets: 20_newsgroups, facilitate the use of learning
SURFACESMATCHINGALGORITHMBASEDONGENETICALGORITHMAN
- 针对基于最小二乘法的ICP 曲面匹配算法难以处理待比较曲面的局部大变形问题, 提出一种改进算 法。即采用遗传算法确定曲面初始相对位置以保证匹配优化结果为全局最优值, 利用ICP 算法匹配结果构造 偏差阈值, 以此阈值过滤点群后再以最小二乘法进行匹配处理, 消除局部大变形影响, 获得合理的变换矩阵。以此变换矩阵变换初始点群再进行误差计算, 从而获得理想的匹配结果-Least square method based on the ICP surface matching algorithm
Ann5
- 基于小波神经网络的时间序列预测的短时交通流量预测的matlab源程序与数据-Based on wavelet neural network time series forecasting short-term traffic flow prediction matlab source code and data
MIMOyuce
- 多变量预测控制源代码。可以用来比较一下和我们的区别。-Multivariable predictive control the source code. Can be used to compare the difference with us.
MATLAB(M)
- 智能控制理论算法实现：层次聚类，hopfield网络和遗传传算法优化三个源代码文件-Intelligent control theory algorithms: hierarchical clustering, hopfield network and genetic algorithm optimization three source codes
expert
- 油气解释专家系统源码，用于油田油气水的测录井解释，使用了遗传学算法-Oil and Gas explain the expert system source code for the oil and gas fields of water logging interpretation using genetic algorithms
NcutClustering
- 谱聚类和SVM相结合的图像分割程序,这是用matlab编的谱聚类程序，该方法能有效的用于数据分类。-This is a matlab procedures for the spectral clustering.The method is effective for data classification.
BPNN
- 该程序包包含三个函数，一个用于建立BP神经网络，一个用于训练该网络，最后一个用于测试样本。-The package contains three functions, one for the establishment of BP neural networks, one for training the network, the last one for the test samples.