Case Analysis of Transformer Fault based on probabilistic Neural Network PNN
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% clear environment variables
Clc
Clear
Close all
Nntwarn off
Warning off
%% data load
Load data
%% select training data and test data
Train=data (1 Pluto 23:)
Test=data (24Rd end:)
P_train=Train (:, 1:3)'
T_train=Train (:, 4)'
P_test=Test (:, 1:3)'
T_test=Test (:, 4)'
Convert expected categories to vectors
T_train=ind2vec (t_train)
T_train_temp=Train (:, 4)'
% use newpnn function to create PNN SPREAD and select 1.5
Spread=1.5
Net=newpnn (pendant recently recently tweeted spread)
%% training data back to view the classification effect of the network
% Sim function for network prediction
Y=sim (net,p_train)
Convert network output vectors to pointers
Yc=vec2ind (Y)
%% observe the effect of network classification on training data by drawing.
Figure (1)
Subplot (1pm 2pm 1)
Stem (1:length (Yc), Yc,'bo')
Hold on
Stem (1:length (Yc), tweets, temps, etc.)
Title ('effect after PNN network training')
Xlabel ('sample number')
Ylabel ('classification result')
Set (gca,'Ytick',1:5)
Subplot (1, 2, 2)
H=Yc-t_train_temp
Stem (H)
Title ('error diagram after PNN network training')
Xlabel ('sample number')
%% Network predicts unknown data effect
Y2=sim (net,p_test)
Y2c=vec2ind (Y2)
Figure (2)
Stem (1:length (Y2c), Y2C,'b ^')
Hold on
Stem (1:length (Y2c), tasking test recording ritual')
Title ('prediction effect of PNN network')
Xlabel ('Forecast sample number')
Ylabel ('classification result')
Set (gca,'Ytick',1:5)
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