资源列表
WOA_Toolbox
- matlab鲸鱼算法工具箱,包含多种无约束测试函数，多种算法进行比较，GUI界面良好，是今年国外学者新提出的仿生智能优化算法-Whale algorithm matlab toolbox contains a variety of unconstrained test functions, a variety of algorithms are compared, a good GUI interface is intelligent bionic year foreign scholars p
QGA
- 本程序对量子遗传算法中的量子旋转门的调整策略进行改进，并应用于旅行商（TSP）的适应度问题-Fitness problems this procedure quantum genetic algorithm quantum revolving door policy adjustment is improved and applied to the traveling salesman (TSP) of
Bi-level-programming-model
- 双层规划模型，遗传算法，多目标优化，源代码-Bi-level programming model of basic Matlab source code
PMSM_SMO
- 永磁同步电机滑膜控制，控制效果比PID好，可以学习一下。-Synovial PMSM control, PID control is better than good, you can learn about.
LightNet-master
- 目前最轻量级别的深度学习源码，采用CNN实现高精度识别，与目前流行的Caffe相比，具有实现灵活，可迁移程度高等优势，值得深入学习！-Currently the lightest level of deep learning source, using CNN to achieve high-precision identification, compared with the current popular Caffe, with the realization of flexible, hi
yu-jiang-Paper
- 这是一篇IEEE论文中的仿真代码，是关于鲁棒自适应动态规划的-this is a IEEE paper about ADP
ewt
- 经验小波变换，一种新的自适应信号处理方法-Empirical wavelet transform, a new adaptive signal processing method
LMDP6Data-expansion
- LMD算法利用极值平均、边界局部特征尺度延拓、HDJ极值延拓法、基于ISBM延拓、平行延拓法等对数据拓展，降低端点效应。-The LMD algorithm uses the extreme value average, the boundary local characteristic scale extension, the HDJ extreme value extension method, based on the ISBM extension, the parallel extens
continuous-time-algorithm
- 多智能体一致性算法，此算法通过交流多个机器人的位置信息，最终实现所有机器人位置的一致。-multi agent consensus algorithm this algorithm can make multi robots to be consensus.
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
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
BP神经网络运动状态分类
- 该程序可以通过训练集对所构建的BP神经网络进行训练，并能通过测试集，即对不同的运动状态进行分类。(The program can train the constructed BP neural network through the training set, and can classify the different motion states through the test set.)