In this article, the authors undertook the task of designing and implementing a fuzzy Perceptron using the arithmetic of Ordered Fuzzy Numbers (OFN). The inputs and weights introduced into the PerceptronOFN were represented as Ordered Fuzzy Numbers, a novel approach in the field. Notably, the authors addressed the challenge of lacking operators for comparing numbers in OFN notation, a hurdle that had hindered the construction of a PerceptronOFN in the past. To overcome this, the authors employed a modified Canberra distance as the OFN numbers comparison operator, a concept they had developed in a previous season of their research. This innovative use of OFN in the implementation of a fuzzy Perceptron presents a unique contribution to the field, offering a new perspective on handling uncertainties in neural network computations.