我正在尝试创建神经网络-非线性时间序列NARX模型
我输入的算法是
1-2D矩阵（x，y）
2个另一个2D矩阵（x，y）
目标是此二维矩阵（x，y）中的实际精确值
首先，我已经搜索并使用MATLAB对这个网络进行了建模，
好的结果，我将在DOWN中显示
但是，我想在PYTHON中实现此模型“ NARX MODEL”
我已经搜索了（NARX MODEL）的算法，但没有得到我想要的eny结果：
1个给我任何参考资料，网站，书籍，视频系列的人
2或向我显示正确搜索此特定任务的方法
3或给我一些步骤，用python编写与** MATLAB“ NARX等效的代码
源代码和功能“ **
这是CODE MATLAB：
% Solve an Autoregression Problem with External Input with a NARX Neural Network
% Script generated by NTSTOOL
% Created Wed Nov 09 20:28:50 EET 2016
%
% This script assumes these variables are defined:
%
% input- input time series.
% output- feedback time series.
inputSeries = tonndata(input,true,false);
targetSeries = tonndata(output,true,false);
% Create a Nonlinear Autoregressive Network with External Input
inputDelays = 1:2;
feedbackDelays = 1:2;
hiddenLayerSize = 10;
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
% Choose Input and Feedback Pre/Post-Processing Functions
% Settings for feedback input are automatically applied to feedback output
% For a list of all processing functions type: help nnprocess
% Customize input parameters at: net.inputs{i}.processParam
% Customize output parameters at: net.outputs{i}.processParam
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.inputs{2}.processFcns = {'removeconstantrows','mapminmax'};
% Prepare the Data for Training and Simulation
% The function PREPARETS prepares timeseries data for a particular network,
% 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.
[inputs,inputStates,layerStates,targets] = preparets(net,inputSeries, {},targetSeries);
% Setup Division of Data for Training, Validation, Testing
% The function DIVIDERAND randomly assigns target values to training,
% validation and test sets during training.
% For a list of all data division functions type: help nndivide
net.divideFcn = 'dividerand'; % Divide data randomly
% The property DIVIDEMODE set to TIMESTEP means that targets are divided
% into training, validation and test sets according to timesteps.
% For a list of data division modes type: help nntype_data_division_mode
net.divideMode = 'value'; % Divide up every value
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% Choose a Training Function
% For a list of all training functions type: help nntrain
% Customize training parameters at: net.trainParam
net.trainFcn = 'trainlm'; % Levenberg-Marquardt
% Choose a Performance Function
% For a list of all performance functions type: help nnperformance
% Customize performance parameters at: net.performParam
net.performFcn = 'mse'; % Mean squared error
% Choose Plot Functions
% For a list of all plot functions type: help nnplot
% Customize plot parameters at: net.plotParam
net.plotFcns = {'plotperform','plottrainstate','plotresponse', ...
'ploterrcorr', 'plotinerrcorr'};
% Train the Network
[net,tr] = train(net,inputs,targets,inputStates,layerStates);
% Test the Network
outputs = net(inputs,inputStates,layerStates);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)
% Recalculate Training, Validation and Test Performance
trainTargets = gmultiply(targets,tr.trainMask);
valTargets = gmultiply(targets,tr.valMask);
testTargets = gmultiply(targets,tr.testMask);
trainPerformance = perform(net,trainTargets,outputs)
valPerformance = perform(net,valTargets,outputs)
testPerformance = perform(net,testTargets,outputs)
% View the Network
view(net)
% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, plotregression(targets,outputs)
%figure, plotresponse(targets,outputs)
%figure, ploterrcorr(errors)
%figure, plotinerrcorr(inputs,errors)
% Closed Loop Network
% Use this network to do multi-step prediction.
% The function CLOSELOOP replaces the feedback input with a direct
% connection from the outout layer.
netc = closeloop(net);
netc.name = [net.name ' - Closed Loop'];
view(netc)
[xc,xic,aic,tc] = preparets(netc,inputSeries,{},targetSeries);
yc = netc(xc,xic,aic);
closedLoopPerformance = perform(netc,tc,yc)
% Early Prediction Network
% For some applications it helps to get the prediction a timestep early.
% The original network returns predicted y(t+1) at the same time it is given y(t+1).
% For some applications such as decision making, it would help to have predicted
% y(t+1) once y(t) is available, but before the actual y(t+1) occurs.
% The network can be made to return its output a timestep early by removing one delay
% so that its minimal tap delay is now 0 instead of 1. The new network returns the
% same outputs as the original network, but outputs are shifted left one timestep.
nets = removedelay(net);
nets.name = [net.name ' - Predict One Step Ahead'];
view(nets)
[xs,xis,ais,ts] = preparets(nets,inputSeries,{},targetSeries);
ys = nets(xs,xis,ais);
earlyPredictPerformance = perform(nets,ts,ys)
这是输入
这是x坐标
1 4 7 9 11 17 14 16 18 19
这是y坐标
1 2 4 6 7 8 10 10 13 14
这是另一个x坐标
1 7 10 13 16 18 19 23 24 25
这是另一个y坐标
1 5 7 9 12 14 16 17 19 20
这是目标
这是实际的x坐标
1 4 5 8 9 15 17 18 20 22
这是实际的y坐标
1 1 4 7 8 10 13 14 18 20
对于两个输入的较大误差，结果都足够好与输出相比
但是通过更改nerouns，我们可以增强此输出
[5.00163468043085;3.99820942369434]
[8.00059395052246;6.99872447652641]
[11.5625431537178;8.00040094120297]
[14.9982223917152;9.24359668634943]
[19.3511330333522;13.0001065644369]
[18.4627579643821;13.9999624796494]
[20.0004073095041;17.9997197490528]
[22.0004822590849;19.9997852867243]
希望那会足够清楚
提前致谢
最佳答案
PyNeurGen是可能的解决方案。
它是一个python库，支持多种网络体系结构。
该库还包含一个前馈网络的演示。
对于使用NARX net，您可以使用以下定义：NARX Net PyNeurGen
关于python - python中的神经网络非线性时间序列Narx模型，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/40834150/