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This article mainly explains "transformer fault case analysis based on probabilistic neural network PNN". The content of the explanation in this article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "transformer fault case analysis based on probabilistic neural network PNN".

% 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)

Thank you for your reading. The above is the content of "Transformer Fault case Analysis based on probabilistic Neural Network PNN". After the study of this paper, I believe you have a deeper understanding of transformer fault case analysis based on probabilistic neural network PNN, and the specific application needs to be verified by practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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