资源列表
BP.m
- BP神经网络建模归一化数据处理神经网络训练分类以及遗传算法优化的BP神经网络函数拟合(BP neural network modeling normalized data processing neural network training classification and genetic algorithm optimization BP neural network function fitting)
2
- 通过随机产生高斯分布数据，来对数据进行分类。(The data are classified by random generation of gaussian distribution data.)
General_of_svm
- svm分类实例。根据所选数据维数，输出最终分类类别。(svm classification example.)
DataLine
- 基于高斯概率模型的分类，这是一个10分类的情况，基于minist数据(pattern classification)
fuzzyPID
- 模糊PID算法，使用模糊算法根据误差和误差变化率调整P、I、D的值，提高响应性能。(Fuzzy PID algorithm, the use of fuzzy algorithm based on the rate of error and error rate adjustment P, I, D value, improve response performance.)
bp-分类器
- 这是bp神经网络的M文件，包括BP网络的第一阶段学习期（训练加权系数wki，wij)，BP网络的第二阶段工作期（根据局训练好的wki，wij和给定的输入计算输出），程序里有详细注释。该程序被用来作为分类器使用。(This is the BP neural network M files, including the first phase of BP network learning period (training weighting coefficient wki, wij), the se
pso
- 应用粒子群算法进行迭代优化，寻找目标最优值(Particle swarm optimization (PSO) algorithm is used for iterative optimization to find the optimal target value)
KMeans
- 基于matlab语言编译的用于实现聚类的k-means函数(K-means function compiled by MATLAB language for clustering)
1.1 - 副本
- 用随机森林对玻璃进行分类 用python集成方法解决多类别分类问题(Classification of glass using random forests)
alpha one
- 人工智能 alpha one 算法，训练30天，效果战胜围棋九段选手(The quadtree level of detail implementation that I described works, but there are many improvements that could be made. As of now, there are three main things that need to be improved or fixed: t-junctions/cracks, p
2pca
- 对数据进行主成分分析，以达到数据的降维。(The principal component analysis is performed to reduce the dimensionality of the data.)
KNN
- 十大经典数据挖掘算法之一knn 利用python实现(One of the data minning algorithm :knn,using python)