Team:Heidelberg/Modeling
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===Neural Network Model=== | ===Neural Network Model=== | ||
====Neural Network theory==== | ====Neural Network theory==== | ||
- | Artificial Neural Network usually called (NN), | + | Artificial Neural Network usually called (NN), is a computational model that is inspired by the biological nervous system. The network is composed of simple elements called artificial neurons that are interconnected and operate in parallel. In most cases the NN is an adaptive system that can change its structure depending on the internal or(and?) external information that flows into the network during the learning process. The NN can be trained to perform a particular function by adjusting the values of the connection, called weights, between the artificial neurons. Neural Networks have been employed to perform complex functions in various fields, including pattern recognition, identification, classification, speech, vision, and control systems. |
- | During the learning process | + | During the learning process, difference between the desired output (target) and the network output is minimised. This difference is usually called cost; the cost function is the measure of how far is the network output from the desired value. A common cost function is the mean-squared error and there are several algorithms that can be used to minimise this function. The following figure displays such a loop. |
[[Image:network.gif|400px|center]] | [[Image:network.gif|400px|center]] | ||
- | Figure 1: | + | Figure 1: Training of a Neural Network. |
====Model description==== | ====Model description==== |
Revision as of 14:04, 25 October 2010