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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), it is a computational model that is inspired by the biological nervous system. The network is composed by 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 external information that flow into the network during the learning process. The NN can be trained to perform a particular function by adjusting the values of the connection (weights) between the artificial neurons. | + | Artificial Neural Network usually called (NN), it is a computational model that is inspired by the biological nervous system. The network is composed by 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 external information that flow into the network during the learning process. The NN can be trained to perform a particular function by adjusting the values of the connection (weights) between the artificial neurons. Neural Network have been trained to perform complex functions in various fields, including pattern recognition, identification, classification, speech, vision, and control systems. |
| - | During the learning process the difference between the desired output (target) and the network output is minimized. 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 minimize this function. | + | |
| + | During the learning process the difference between the desired output (target) and the network output is minimized. 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 minimize this function. In the following figure is showed such a loop. | ||
[[Image:network.gif|400px|center]] | [[Image:network.gif|400px|center]] | ||
Figure 1: Normally Neural Networks are trained so that a particular input leads to a specific target output. | Figure 1: Normally Neural Networks are trained so that a particular input leads to a specific target output. | ||
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====Model description==== | ====Model description==== | ||
Revision as of 11:51, 25 October 2010
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