% Solve an Autoregression Problem with External Input with a NARX Neural
Network
解决一个
NARX
神经网络的外部输入自回归问题
% Script generated by Neural Time Series app
脚本由神经时间序列程序生成
% Created 28-Mar-2017 10:42:15
%
% This script assumes these variables are defined:
此脚本假设以下变量已定义：
%
% in - input time series.in-
输入时间序列
% out - feedback time series.out-
反馈时间序列
%tonndata/fromnndata
把数据转化为标准神经网络格式
X = tonndata(in,false,false);
T = tonndata(out,false,false);
% Choose a Training Function
选择一个训练功能
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn =
'trainlm'
;
% Levenberg-Marquardt backpropagation.
% Create a Nonlinear Autoregressive Network with External Input
inputDelays = 1:2;
feedbackDelays = 1:2;
hiddenLayerSize = 10;
net =
narxnet(inputDelays,feedbackDelays,hiddenLayerSize,
'open'
,trainFcn);
% Choose Input and Feedback Pre/Post-Processing Functions
选择输入和反馈前
/
后处理功能
% Settings for feedback input are automatically applied to feedback output
反馈输入的设置将自动应用于反馈输出
% For a list of all processing functions type: help nnprocess
%
Customize
input
parameters
at:
net.inputs{i}.processParam
删除矩阵的行定值。
映射矩阵行的最小值和最大值为
[ 1 - 1 ]
% Customize output parameters at: net.outputs{i}.processParam
删除矩阵的行定
值。映射矩阵行的最小值和最大值为
[ 1 - 1 ]
net.inputs{1}.processFcns = {
'removeconstantrows'
,
'mapminmax'
};
net.inputs{2}.processFcns = {
'removeconstantrows'
,
'mapminmax'
};
% Prepare the Data for Training and Simulation
准备数据用于训练和模拟
%
The
function
PREPARETS
prepares
timeseries
data
for
a
particular
network,
preparets
功能，为一个特定的网络准备时间序列数据，
% shifting time by the minimum amount to fill input states and layer
%
states.
Using
PREPARETS
allows
you
to
keep
your
original
time
series
data
% unchanged, while easily customizing it for networks with differing
% numbers of delays, with open loop or closed loop feedback modes.
移动时间的最小量从而填写输入状态和层的状态。
使用
PREPARETS
使你可以保存原时间序列数据不
变，同时更易定制不同的网络延迟，通过开环或闭环反馈模式。
[x,xi,ai,t] = preparets(net,X,{},T);
% Setup Division of Data for Training, Validation, Testing
数据分割用于训练，
验证，测试
% For a list of all data division functions type: help nndivide
