How to use matlab Neural Network to fit nonlinear function
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%% construct fitting data
For iTunes 114000
Input (iPermine:) = 10*rand (1penny 2)-5
Output (I) = input (iMagne 1) ^ 2+input (iMagazine 2) ^ 2
End
Output=output'
Save data input output
% clear environment variables
Clc
Clear
Tic
%% training data prediction data extraction and normalization
Download input and output data
Load data input output
% randomly sorted from 1 to 2000
K=rand (1JI 4000)
[mmaine n] = sort (k)
% find training data and forecast data
Input_train=input (n (1 3900),:)'
Output_train=output (n (1 3900),:)'
Input_test=input (n (3901 4000),:)'
Output_test=output (n (3901 4000),:)'
% selected sample input and output data normalization
[inputn,inputps] = mapminmax (input_train)
[outputn,outputps] = mapminmax (output_train)
%% BP network training
Initialize network structure
Net=newff (inputn,outputn,5)
Net.trainParam.epochs=100
Net.trainParam.lr=0.1
Net.trainParam.goal=0.0000004
% Network training
Net=train (net,inputn,outputn)
% BP Network Forecast
% prediction data normalization
Inputn_test=mapminmax ('apply',input_test,inputps)
% network forecast output
An=sim (net,inputn_test)
% network output is de-normalized
BPoutput=mapminmax ('reverse',an,outputps)
%% result analysis
Figure (1)
Plot (BPoutput,':og')
Hold on
Plot (output_test,'-*')
Legend ({'predicted output', 'expected output'}, 'fontsize',12)
Title ('BP network prediction output', 'fontsize',12)
Xlabel ('sample', 'fontsize',12)
Ylabel ('output', 'fontsize',12)
Print-dtiff-r600 4-3
% prediction error
Error=BPoutput-output_test
Figure (2)
Plot (error,'-*')
Title ('neural network prediction error')
Figure (3)
Plot ((output_test-BPoutput). / BPoutput,'-*')
Title ('neural network prediction error percentage')
Errorsum=sum (abs (error))
Toc
Save data net inputps outputps
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