Artificial neural network: Difference between revisions
Jump to navigation
Jump to search
imported>Felipe Ortega Gutiérrez No edit summary |
imported>Felipe Ortega Gutiérrez No edit summary |
||
| Line 1: | Line 1: | ||
'''Artificial Neural Network''' (ANN for short) is a processing model inspired on the biological neural networks. Artificial neural networks are composed by simple nodes called [[artificial neurons]] and the processing behavior is stored in the node interconnections as | '''Artificial Neural Network''' (ANN for short) is a processing model inspired on the biological neural networks. Artificial neural networks are composed by simple nodes called [[artificial neurons]] and the processing behavior is stored in the node interconnections as ''weights''. | ||
==Adaptation and Learning== | ==Adaptation and Learning== | ||
When a neuron receives and processes an input signal, it changes its behavior by changing its threshold and/or weight values, producing also a change in the entire network. Since artificial neurons have a predictable behavior, artificial neural networks can be trained by being fed with sequences of inputs, often determined by certain functions. Besides, there are neural networks which do not require training. | When a neuron receives and processes an input signal, it changes its behavior by changing its threshold and/or weight values, producing also a change in the entire network. Since artificial neurons have a predictable behavior, artificial neural networks can be trained by being fed with sequences of inputs, often determined by certain functions. Besides, there are neural networks which do not require training. | ||
Revision as of 23:29, 13 April 2007
Artificial Neural Network (ANN for short) is a processing model inspired on the biological neural networks. Artificial neural networks are composed by simple nodes called artificial neurons and the processing behavior is stored in the node interconnections as weights.
Adaptation and Learning
When a neuron receives and processes an input signal, it changes its behavior by changing its threshold and/or weight values, producing also a change in the entire network. Since artificial neurons have a predictable behavior, artificial neural networks can be trained by being fed with sequences of inputs, often determined by certain functions. Besides, there are neural networks which do not require training.