Abstract:

Nowadays there are many different models of artificial neural networks. The difference between these models lies in the learning methods only, that is, in the rules for changing the parameters of the algorithm tuning, with or without links and side comments. While studying the general framework of network models, we see that the rules for obtaining the result and the mechanism for calculating the error can differ. For example, a multilayer realization might produce a threshold function when used as a classifier, or a linear function if used as an internal typeface.   Where this research came to discuss the possibilities of the standard representation of some models of artificial neural networks, which clarify and treat some of the characteristics of that representation. Which can be considered an essential element in the process of typical representation of these networks, where a new proposal is made during this representation by using a “layer” of neurons, in other words, using a group of neurons that work in parallel and perform the same functions for which they were set .