资源列表
inverted-pendulum-control
- 利用强化学习的自适应动态规划中的值迭代和策略迭代方法，神经网络控制方法，LQR状态调节器最优控制方法，实现了三维倒立摆在飞行器上的稳定控制。鲁棒性很强，进行了高斯白噪声的扰动实验。-Reinforcement learning adaptive dynamic programming in value iteration and policy iteration method, neural network control method, LQR state regulator optimal
multi-agent
- 多智能体算法带时延的一致性验证以及画图，部分内容有注释-Multi-agent algorithm with delay compliance verification and drawing, part of the Notes
TIG_ARC
- 钨极氩弧焊流体计算，TIG电弧源项，电场，UDF- U94A8 u6781 u6C29 u5F27 u710A u6D41 u4F53 u8BA1 u7B97 uFF0CTIG u7535 u5F27 u6E90 u9879 uFF0C u7535 u573A uFF0CUDF
《神经网络与深度学习》讲义-邱锡鹏
- 《神经网络与深度学习》讲义-邱锡鹏 一个人在不接触对方的情况下，通过一种特殊的方式，和对方进行 一系列的问答。如果在相当长时间内，他无法根据这些问题判断对方是 人还是计算机，那么就可以认为这个计算机是智能的。(Lectures on neural networks and deep learning - Qiu Xipeng A person who has no contact with each other has a series of questions and answers wi
改进的禁忌搜索算法
- 改进的禁忌搜索算法，求解路径寻优问题，相对传统的遗传算法，能够更精确求得全局最优解(Compared with the traditional genetic algorithm, the improved tabu search algorithm can solve the problem of path optimization, and can obtain the global optimal solution more accurately)
ELM
- MATLAB版极限学习机，自带训练集与测试集，可运行。(MATLAB version of the ELM, with its own data set)
改进的GA优化BP
- 改进的遗传算法优化的BP神经网络，用于电厂数据的异常检测和故障诊断，已验证有效性。(The improved genetic algorithm optimized BP neural network has been validated for power plant data anomaly detection and fault diagnosis.)
CNN
- 卷积神经网络matlab代码,内附详细解释。可用于图像识别，数据分类。(this is an zip of matlab code for CNN used for data classification or figure recognization.)
程序
- 基于matlab无人机编队模型程序，数学建模大赛专用，适合初学者，可以入门(Based on the MATLAB unmanned aerial vehicle formation model program, mathematical modeling competition special, suitable for beginners, can be introduced)
代码
- MATLAB分数阶参数设计，控制参数研究混合粒子群算法优化分数阶PID(MATLAB fractional order parameter design)
斯坦福大学-深度学习基础教程
- 斯坦福大学的深度学习，基础教程。ps:要求先学完吴恩达的机器学习基础再来看，不然看不懂(Depth study of Stanford University, basic course. Ps: needs to learn the basis of Wu Enda's machine learning first, or you can't understand it.)
预测
- BP神经网络模型预测的Python代码实现，测试有效(BP neural network model forecast Python code implementation, test effectiveness)