Neural Networks: Difference between revisions
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== Questions and Answers == |
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* '''What is an neural network?''' |
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An artificial neural network is a mathematical model of an associative biological neural network. |
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* '''What are the differences between a biological and an artificial neural network?''' |
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Between both networks are huge differences. The neurons of a biological neural networks works parallel massively and such a network has billions or trillions of neurons connected massively. In comparison the artificial neural networks has just a few count of neurons depending on the among of data to associate by the network, e.g. 20 neurons. Such an artificial network is not connected by accident, but as a controlled and planned layer structure, where is an input layer, one or a few hidden layers and an output layer (if a multilayer perceptron topology is used). As an artificial neural network is a computer software it works sequentially and therefore there is a big difference to a biological neural network, of course. |
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* '''What is the benefit of an artificial neural network?''' |
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The big advantage of human brain is the adaption of seen unknown things to a learned model stored in the brain's neurons. This is called association. Technically it is called an unsharpened information processing, so an artificial neural network can be used in an environment, where input information is not complete (like face recognition). |
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* '''What are examples for technical use of artificial neural networks?''' |
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There are a few very interesting uses of artificial neural networks. For example, the local electricity factory in Duesseldorf, Germany is using an multilayer perceptron for daily prediction of power use in the city referring to temperature, humidity, day, etc. Another example is the prediction how many articles of a product in a supermarket will be sold in one week. With this information it is possible to calculate much better the use of the articles ordering and storage. Or such a network is used to predict the among of daily calls in a call centre to plan how many co-workers have to work that day. Or the German Post is using neural networks in recognition of post codes (PLZ) written on letters by computer or manually. After recognition a computer readable code is printed on the letter so further recognition is not necessary again. |
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== Information about Neural Networks == |
== Information about Neural Networks == |
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Revision as of 09:30, 21 December 2007
Questions and Answers
- What is an neural network?
An artificial neural network is a mathematical model of an associative biological neural network.
- What are the differences between a biological and an artificial neural network?
Between both networks are huge differences. The neurons of a biological neural networks works parallel massively and such a network has billions or trillions of neurons connected massively. In comparison the artificial neural networks has just a few count of neurons depending on the among of data to associate by the network, e.g. 20 neurons. Such an artificial network is not connected by accident, but as a controlled and planned layer structure, where is an input layer, one or a few hidden layers and an output layer (if a multilayer perceptron topology is used). As an artificial neural network is a computer software it works sequentially and therefore there is a big difference to a biological neural network, of course.
- What is the benefit of an artificial neural network?
The big advantage of human brain is the adaption of seen unknown things to a learned model stored in the brain's neurons. This is called association. Technically it is called an unsharpened information processing, so an artificial neural network can be used in an environment, where input information is not complete (like face recognition).
- What are examples for technical use of artificial neural networks?
There are a few very interesting uses of artificial neural networks. For example, the local electricity factory in Duesseldorf, Germany is using an multilayer perceptron for daily prediction of power use in the city referring to temperature, humidity, day, etc. Another example is the prediction how many articles of a product in a supermarket will be sold in one week. With this information it is possible to calculate much better the use of the articles ordering and storage. Or such a network is used to predict the among of daily calls in a call centre to plan how many co-workers have to work that day. Or the German Post is using neural networks in recognition of post codes (PLZ) written on letters by computer or manually. After recognition a computer readable code is printed on the letter so further recognition is not necessary again.
Information about Neural Networks
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