资源列表
Machine Learning
- 作者是Prateek Joshi.人工智能专家。本书注重于对基于机器学习中深度学习内容的分析，并附上了许多经典案例，非常值得一读。(The writer is an expert in Prateek Joshi. AI. This book focuses on the analysis of in-depth learning in machine-based learning, and attaches many classic cases, which are worth reading
免疫优化算法
- 是分布式电源的选址定容的免疫优化算法算法，是最基本的遗传算法，基本实现了选址定容的功能(It is an inheritance algorithm for location and capacity of distributed power supply and the most basic genetic algorithm. It basically realizes the function of location and capacity determination.)
matlab Q学习仿真
- 基于Q学习算法，寻找最优路径，是强化学习中的一种，很实用，代码很详细，有备注(Searching for the optimal path based on Q algorithm is a kind of reinforcement learning. It is very practical, the code is very detailed, with notes.)
代理模型工具箱
- 里面包含很多代理模型，多种模型可选择，可根据实际情况构建较为精确的模型
Deep-Belief-Network-for-Regression-master
- 利用深度置信网络进行回归建模(Regression Modeling Using Deep Belief Network)
DBN-ELM-master
- DBN-ELM应用于软测量，可以预测精度，并且可以和其它软测量模型进行对比。(Application of DBN-ELM in Soft Sensing,it can predict the accuracy and compare with other soft sensor models.)
粒子群算法优化RBF网络
- 粒子群算法优化RBF网络，有相关解释，matlab源码！可以跑通(Particle swarm optimization RBF network, there are relevant explanations, matlab source code! You can run through.)
神经网络在电力负荷预测
- 附件中包括了制作的关于matlab神经网络在电力负荷预测中的应用的ppt，并且包括程序，希望大家能都锻炼下(The appendix includes the PPT produced about the application of MATLAB neural network in power load forecasting, and includes the program. I hope you can all exercise it.)
简化模糊神经网络MATLAB代码，对初学者有一定用处
- 适合初学者使用的模糊神经网络，基于matlab平台。(Suitable for beginners)
Python Neo4j医药知识图谱自动问答系统源码
- Python+Neo4j医药知识图谱自动问答系统源码，知识图谱构建，自动问答，基于kg的自动问答。以疾病为中心的一定规模医药领域知识图谱，并以该知识图谱完成自动问答与分析服务。(Python+Neo4j Medical Knowledge Atlas Automatic Question and Answer System source code, knowledge atlas construction, automatic question and answer, based on kg a
小波神经网络预测代码
- 小波神经网络预测的完整的MATLAB代码。有数据，可直接运行实现。(Wavelet neural network prediction of complete MATLAB code. With data, you can run the implementation directly.)
svmtrain
- matlab系统原来自带的svmtrain函数和svmclassify函数。 有需要的在2017a以前的版本里面也能找到！ 在新版本中（2018以后），你再运行有svmtrain函数的文件，我试了一下结果是，就算你拷贝过来原来的svmtrain函数，还是运行不出来。。所以各位可以下载试一下。。我建议还是转为最新支持的fitcsvm函数吧！(svmtrain and svmclassify function)