Paraconsistent Repetition Learning System Applied to ATMEGA 328p Chip Paulino Machado Gomes, João Inácio da Silva Filho

Abstract

This article aims to present a modeling of Paraconsistent Logic through a Paraconsistent Artificial Neural Cell of Physical Learning (CNAPap). Constructed from a basic Paraconsistent Cell, represented by the Algorithm that describes the lattice associated with the Annotated Paraconsistent Logic called “Para-Analyzer” (Da Silva Filho, 2006). In this work a Paraconsistent Physical Learning Cell is developed by inserting the mathematical model originated from the algorithm into a Chip, implemented by a structured programming language. Through testing and modeling using the learning algorithm, it is demonstrated that a CNAPap can learn and save patterns with characteristics similar to Biological Neurons in the human brain. The results of the tests and modeling using the learning algorithm are presented at the end of this work, demonstrating the functional characteristics of CNAPap.