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This article mainly introduces the relevant knowledge of "matlab how to realize self-organizing competitive neural network". Xiaobian shows you the operation process through actual cases. The operation method is simple, fast and practical. I hope this article "matlab how to realize self-organizing competitive neural network" can help you solve the problem.

The competitlayer function creates a competing network layer that classifies input samples based on similarity between them, with a given number of classes, and always tends to assign the same number of samples to each class, classifying as evenly as possible.

inputs = iris_dataset;

% Load Data

net = competlayer(3);

% Create Competitive Network

net = train(net,inputs);

% Training

outputs = net(inputs);

% classification

classes = vec2ind(outputs);

% format conversion. classes for classification results

The selforgemap function clusters data using similarities and topology of the data itself.

x = simplecluster_dataset;

figure

plot(x(1,:),x(2,:),'o')

set(gcf,'color','w')

title ('raw data')

net = selforgmap([8 8]);

% Create Self-Organizing Mapping Network

net = train(net,x);

% Training

y = net(x);

classes = vec2ind(y);

figure

hist(classes,64)

% Show cluster results

set(gcf,'color','w')

title ('Cluster results')

xlabel ('Category ')

ylabel ('Number of samples contained in category')

figure

plotsompos(net,x)

% Show the location of the category center point

net = selforgmap([2,3]);

net = train(net,x);

y = net(x);

classes = vec2ind(y);

figure

hist(classes,6)

Number of samples in % 6 categories

figure

plotsomhits(net,x)

% shows the number of each category

figure

plotsompos(net,x)

% Show the location of the category center point

Data files are MATLAB's own data for clustering

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