Difference between revisions of "Examples"

 
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== Logical Functions ==
 
== Logical Functions ==
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Training an artificial neural network to learn logical functions is just interesting in learning the use of such a network, but not for practical use. The only interesting thing behind learning the XOR function is that in history of development of neural networks it was figured out the XOR function cannot be learned by just one neuron. But in the past it was quite difficult mathematically to find a solution to connect a few neurons together.
   
 
* [[logical XOR function]]
 
* [[logical XOR function]]
* [[logical OR function]]
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* logical OR function
* [[logical AND function]]
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* logical AND function
   
 
== Prediction ==
 
== Prediction ==
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One benefit of multilayer perceptron is the possibility of prediction.
   
 
* [[Selling Icecreams]]
 
* [[Selling Icecreams]]
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* [[Daily power consumption]]
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* [[Lottery]]
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== Strings ==
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* [[Detection of language]]
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== Classification ==
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* [[Detection of language with classification]]
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== Input Support Classes ==
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* [[Using date input support class]]
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== Optimization ==
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* [[Internet routing decision]]
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== Several functions of ANN ==
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* [[Visual network topoloy]]
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* [[Logging network weights]]
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* [[Client-Server model]]

Latest revision as of 19:26, 6 January 2010

Logical Functions

Training an artificial neural network to learn logical functions is just interesting in learning the use of such a network, but not for practical use. The only interesting thing behind learning the XOR function is that in history of development of neural networks it was figured out the XOR function cannot be learned by just one neuron. But in the past it was quite difficult mathematically to find a solution to connect a few neurons together.

Prediction

One benefit of multilayer perceptron is the possibility of prediction.

Strings

Classification

Input Support Classes

Optimization

Several functions of ANN