资源列表
SOM-dataget
- 基于SOM算法的采油机故障检测，对比其他短发有很大的优点，适合初学者学习！-Production machine fault detection based on SOM algorithm, compared with other hair has a lot of advantages, for beginners learning!
Hopfield2
- 某机构对20所高校的科研能力进行了调研和评价，试根据调研结果中较为重要的11个评价指标的数据，并结合离散Hopfield神经网络的联想记忆能力，建立离散Hopfield高校科研能力评价模型。-A mechanism for 20 universities scientific research ability of research and uation, test according to the research results of 11 important uation index
smokedetection
- 用VS2010和OPENCV编写的关于视频烟雾检测的程序，运行效果很好-With VS2010 and OPENCV prepared on the video smoke detection procedures, running very well
fdICA
- 本代码主要提供了在频域使用fastica进行盲源分离，并且解决了频域的排列和增益两个歧义性问题。-This code mainly provides the use of fastica in the frequency domain for blind source separation, and solves the frequency domain arrangement and gain of two ambiguity problems.
F16_dyn
- F16战斗机四元素法下飞机动力学方程,可以在MATLAB环境下条用进行仿真-the model of F16 fighter aircraft dynamics equation under four element method
neutral-network-tensorflow
- 使用tensorflow实现几类深度学习，如卷积神经网络、自回归神经网络、动态神经网络等-Use tensorflow to achieve several kinds of deep learning, such as convolution neural network, recurrent neural network, dynamic neural network
advanced-function-tensorflow
- 使用tensorflow来实现更高级的功能，例如多gpu并行计算、tensorboard可视化-Tensorflow to use more advanced features, such as multi-gpu parallel computing, tensorboard Visualization
gongjiaopaibanxitong
- 基于遗传算法的公交排版系统分析，包括公交线路模型仿真和公交排版问题模型设计两部分，均可直接运行-Based on the genetic algorithm of public transit system analysis, including bus line model simulation and bus layout problem model design two parts, can be run directly
IGA
- 采用改进的遗传算法对城市交通信号优化分析，对城市道路交叉口交通信号灯实施合理优化控制，可直接运行-Using the improved genetic algorithm to optimize the urban traffic signal analysis, the urban road intersection traffic lights to implement a reasonable optimal control, can be run directly
SSC_ADMM_v1.1
- 稀疏子空间聚类，利用稀疏自表示模型对数据聚类分析。-Sparse Subspace Clustering(SSC) by self-expressive model
RandomForest
- 机器学习随机森林源码。改变决策树的深度对比分类结果。对鸢尾花数据进行决策树分析-random forest
nature-deep-learning
- 世界顶级杂志《自然》，针对人工智能的深度学习进行的最全面综合论述，以及对未来深度学习及神经网络的发展预测，值得一读！-The world s top magazine nature , for the depth of artificial intelligence to learn the most comprehensive exposition, as well as the future development of deep learning and neural network p