In recent years, the fusion of fuzzy logic with neural networks emerges as a promising pathway for tackling complex problems in artificial intelligence and pattern recognition. Fuzzy Neural Networks constitute an innovative fusion of two distinct paradigms, cultivating a pioneering approach to computations that merges the precision of neural networks with the inherent capability for adaptation and inference of fuzzy logic. One of the intriguing concepts of fuzzy logic is the notation and arithmetic of Ordered Fuzzy Numbers. Implementing this enables the acceleration of prediction processes or reduction in algorithmic computational complexity. Thus, it was natural to attempt to construct a fuzzy perceptron using the arithmetic of Ordered Fuzzy Numbers. This article presents the concept of fuzzy networks and the fuzzy perceptron. The implementation resulted in intriguing findings, which are discussed in the conclusions within this article.