资源列表
GA-BP
- 遗传算法优化BP神经网络程序，利用遗传算法找出最优的权值和阈值赋予BP神经网络并开始训练。-Genetic algorithm optimization BP neural network program, the use of genetic algorithm to find the optimal weights and thresholds given BP neural network and start training.
GMDH
- GMDH神经网络程序代码，是非线性函数完全实现的部分表达，可用于多元建模。-GMDH neural network program code, is part of the expression nonlinear function fully implemented, it can be used for multivariate modeling.
6
- SOM神经网络的数据分类 非常具体的实验报告 根据SOM神经网络相关知识，设计一个具有数据分类功能的自组织映射神经网络。要求该网络可以正确地对样本中包含的数据集进行分类。-Data SOM neural network classification very specific test report SOM neural network based on knowledge, to design a self-organizing map data classification ne
ML
- 这是斯坦福大学公开课的笔记，机器学习的同学可以看一下，还是非常不错的。-This is the Stanford University open class notes, machine learning students can look at, or very good.
anneal1
- 模拟退火算法，这是人工智能和机器学习中重要的算法。由C语言编写。可以运行.-Simulated annealing algorithm, which is an important algorithm in artificial intelligence and machine learning. Written by C language. It works well!
BP
- 误差返传神经网络的原始代码非工具箱，适合初学者-The original code error back propagation neural network non toolbox, suitable for beginners
chapter4
- 这是一个基于神经网络遗传算法的系统极值寻优的代码-this is a code of genetic algorithm to calculate a extrme value of the system based on matlab
genetic_BP
- 此代码是用遗传算法来优化BP神经网络的代码。-This code is to use genetic algorithm to optimize the BP neural network code
meunier08age
- 人脑功能网络。利用小波相关，发现年轻被试和老年被试都具有显著优于随机网络的模块性,但随着年龄老化, 脑功能网络模块化结构和各模块内节点地位会逐渐发生变化-Age-related changes in modular organization of human brain functional networks ARTICLE in NEUROIMAGE · DECEMBER 2008 Impact Factor: 6.36 · DOI: 10.1016/j.neuroimage.20
perceptron
- 感知机的c实现，感知机的三要素：模型、学习策略、学习算法，简单易懂，供交流学习。-Perceptron c realization, perceived three elements machine: model, learning strategy, learning algorithm, easy to understand, for the exchange of learning.
RecastNavigation
- recast navigation实现基于unity的导航网格数据进行寻路,优化路径平滑算法-recast navigation realization pathfinding grid-based unity of navigation data, the optimal path smoothing algorithm
RBF_FZ
- 本程序为RBF神经网络预测。输入量为多个，输出为单个，可根据自己需求进行修改。预测效果明显，较为实用。-This procedure is RBF neural network prediction. The input quantity is many, the output is a single, may according to own need to carry on the revision. Prediction effect is obvious, more practical.