资源列表
monituihuoTSP
- 一个模拟退火算法的程序，使用matlab编写。实现了tsp问题的求解-a simulated annealing procedures, the use of Matlab prepared. Implementation of the solution tsp
ASTAR
- implementation od A* algorithm for tiling problem in C
kalman
- \kalman的相关预测和误差程序，推进给新手使用-\ Kalman prediction and errors related to procedures, to promote new users
neuralnet
- 神经网络应用实例，以及它的一个优化。很实用的例子-neural net
ANN
- 编写BP神经网络系统，可适应多节点输入输出的人工神经网络训练，即可以自由定义输入节点的个数，隐层节点个数，输出节点个数以及样本的个数。-Preparation of BP neural network system can be adapted to multi-node input and output of the artificial neural network training, that is free to define the number of input nodes, hid
PID-control-based-on-RBF
- REF网络为三层向前网络，极大的加快了学习速度并避免了局部极小的问题。-Adaptive PID control based on RBF Identification
zhuyuanfenxi123
- 利用主元分析的方法提取车牌字符的有效特征，为下一步输入神经网络识别做准备。-Using principal component analysis method to extract effective features of the license plate to prepare for the next input of the neural network recognition.
PSO2
- 标准粒子群优化算法优化（PSO），该代码可以解决高纬空间，包括数据的预处理，及边界检查。可以应用到数据聚类，分类等问题。-Standard particle swarm optimization algorithm optimization (PSO), the code can solve the high-latitude space, including pre-processing of the data, and bounds checking. Can be applied to d
sa
- 这是本人在做项目时编写的一个模拟退火算法。利用该算法可以求解无约束优化问题。对于约束优化问题，可做相应的修改，即可使用。-The Simulated Annealing program is coded in Matlab. You can use the program to solve the unconstrained optimization program.
CPSO
- 混沌粒子群算法，求代价函数问题，可以避免粒子群算法陷入局部最优，求取全局最优。代码含代价函数，可作为例子理解。-Chaos particle swarm optimization, seeking cost function problems, avoid getting into local optimum particle swarm algorithm, obtaining the global optimum. Code containing cost function can be u
BPJS
- BP的应用实例，是利用自身数据与属性数据预测后期数据的代码，该代码仅仅提供了BP的应用实例，其他算法思路可以自己进行修改-BP' s application example is the use of the code of their own data and attribute data to predict late data, the code only provides application examples of BP, other algorithms can modify
sklearn-xgboost
- sklearn-xgboost sklearn-xgboost的使用以及创建，这个是学习机器学习时的作业，希望大家指正-sklearn-xgboost sklearn-xgboost use and create, this is the time to learn the job of machine learning, I hope you correct