文件名称:anymin
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介绍说明－－下载内容均来自于网络，请自行研究使用
本程序使用GMDH网络对交通的流量进行预测，输入的数据为连续n天的m组流量数据。
输出数据为第n+1天的m组的流量的预测数据。
每个神经元的学习方式为widrow-hoff
在学习过程中，每一层否挑选15个优秀的神经元保留到下一层(This procedure uses GMDH network traffic flow prediction, the input data for the continuous n days of M group traffic data.
The output data are forecast data for the n+1 day traffic of the m group.
Each neuron has a learning style of Widrow-Hoff
In the course of the study, each layer chooses 15 outstanding neurons to hold to the next level)
输出数据为第n+1天的m组的流量的预测数据。
每个神经元的学习方式为widrow-hoff
在学习过程中，每一层否挑选15个优秀的神经元保留到下一层(This procedure uses GMDH network traffic flow prediction, the input data for the continuous n days of M group traffic data.
The output data are forecast data for the n+1 day traffic of the m group.
Each neuron has a learning style of Widrow-Hoff
In the course of the study, each layer chooses 15 outstanding neurons to hold to the next level)
(系统自动生成,下载前可以参看下载内容)
下载文件列表
anymin
anymin\.idea
anymin\.idea\misc.xml
anymin\.idea\modules.xml
anymin\.idea\pycharmcode.iml
anymin\.idea\workspace.xml
anymin\allcoefficient.csv
anymin\allcoefficient.txt
anymin\ann.py
anymin\data.csv
anymin\data.txt
anymin\data1.csv
anymin\dataout.csv
anymin\data_oneyear.csv
anymin\data_oneyear.xlsx
anymin\function.py
anymin\out1.png
anymin\out2.png
anymin\out3.png
anymin\out4.png
anymin\textoutput.csv
anymin\textoutput.txt
anymin\trainoutput.csv
anymin\trainoutput.txt
anymin\__pycache__
anymin\__pycache__\function.cpython-36.pyc
anymin\.idea
anymin\.idea\misc.xml
anymin\.idea\modules.xml
anymin\.idea\pycharmcode.iml
anymin\.idea\workspace.xml
anymin\allcoefficient.csv
anymin\allcoefficient.txt
anymin\ann.py
anymin\data.csv
anymin\data.txt
anymin\data1.csv
anymin\dataout.csv
anymin\data_oneyear.csv
anymin\data_oneyear.xlsx
anymin\function.py
anymin\out1.png
anymin\out2.png
anymin\out3.png
anymin\out4.png
anymin\textoutput.csv
anymin\textoutput.txt
anymin\trainoutput.csv
anymin\trainoutput.txt
anymin\__pycache__
anymin\__pycache__\function.cpython-36.pyc