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請問吧里有大神做過MATLAB時間序列神經網絡(NARX)嗎？請教一下該神經網絡的預測問題

我用網上的一個案例：知道2015年降雨我要預測該年水位。用往年的降雨與水位數據訓練好網絡后該怎么做預測。

代碼如下；在這之后該怎么加載訓練好的網絡以及新的輸入來預測輸出呢？請高手解答一下，謝謝！

clear all

% Solve an Autoregression Problem with External Input with a NARX Neural Network

% Script generated by Neural Time Series app

% Created Thu Jun 08 14:20:08 CST 2017

%

% This script assumes these variables are defined:

%

% rain - input time series.

% water - feedback time series.

rawData=xlsread('R26-3.xlsx','sheet1','B2:D241');

rain=rawData(:,3);

water=rawData(:,2);

X = tonndata(rain,false,false);

T = tonndata(water,false,false);

% Choose a Training Function

% 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. NTSTOOL falls back to this in low memory situations.

trainFcn = 'trainlm'; % Levenberg-Marquardt

% Create a Nonlinear Autoregressive Network with External Input

inputDelays = 1:4;

feedbackDelays = 1:4;

hiddenLayerSize = 10;

net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,'open',trainFcn);

% 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.

[x,xi,ai,t] = preparets(net,X,{},T);

% 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 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,x,t,xi,ai);

% Test the Network

y = net(x,xi,ai);

e = gsubtract(t,y);

performance = perform(net,t,y);

總結

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