Paraconsistent Learning Artificial Neural Cell (CNAPap) Applied in Graphical User Interface Software Leonardo do Espirito Santo, Rodrigo Silvério da Silveira, João Inácio da Silva Filho, Cláudio Luís Magalhaes Fernandes

Abstract

Paraconsistent Artificial Neural Cells (PNACs) are based on the Annotated Paraconsistent Logic with two-valued annotation (LPA2v) and were built to offer characteristics similar to those of a biological neuron. When grouped, these cells allow the creation of Paraconsistent Artificial Neural Networks (PNAs) in order to present the functionalities of a human brain. This work presents the modeling of a Learning Paraconsistent Artificial Neural Cell (PNAC) in a graphical interface software. This interface allows the configuration of a Pattern and a Learning Factor, in which each reinsertion of the pattern generates a new learning result. All these data are monitored through an online graph that can be adjusted when necessary.