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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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