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This article Xiaobian for you to introduce in detail "matlab how to achieve single-layer competitive neural network data classification", the content is detailed, the steps are clear, and the details are handled properly. I hope this "matlab how to achieve single-layer competitive neural network data classification" article can help you solve your doubts, following the editor's ideas slowly in-depth, together to learn new knowledge.

In the case of no supervision and no expected output, the neural network based on tutor learning is often powerless. Self-organizing neural network can be observed, analyzed and compared repeatedly to objective events, and the glyph problem is its inherent rule. And correctly classify things with common characteristics, this kind of network is more similar to the learning mode of biological neural network in human brain, that is, it can be self-organized by automatically finding the inherent laws and essential attributes in the samples. adaptively change the parameters and structure of the network, which is also the origin of self-organizing name. the learning rules of self-organizing neural networks mostly adopt competitive learning rules.

The basic idea of the competitive neural network is that each neuron in the competitive layer of the network competes to get the opportunity to respond to the input pattern through competition. in the end, only one neuron becomes the winner of the competition. and the weight of each connection related to the winning neuron is adjusted to a direction that is more conducive to its competition, and the self-organizing competitive network can learn adaptively. It further broadens the application of neural network in pattern classification and recognition.

% clear environment variables

Clc

Clear

% enter input data

Load data and divide the data into two categories: training and prediction

Load gene.mat

Data=gene

P=data (1Pluto 40Di:)

T=data (41PUR 60J:)

% is in accordance with the input format of neural network after transpose.

Please P'

Trout'

%% Network Establishment and training

Establish a competitive network:

Net = competlayer (2)

% initialize the network and set network parameters:

Net=init (net)

Net.trainparam.epochs=20

% training network:

Net=train (net,P)

Effect verification of%% network

% bring back the original data to test the network effect:

A=sim (net,P)

Ac=vec2ind (a)

%% network for classified prediction

Below, bring the last 20 data into the neural network model and observe the network output:

Y=sim (net,T)

Yc=vec2ind (Y)

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