Using Fuzzy Feed-Forward Neural Network for Linguistic Processing

Abstract

Fuzzy sets have been implemented efficiently to manage unclear data, language terms, and
vague notions. Recently, considerable work has been dedicated to merging neural-network
techniques with fuzzy sets. In this study, present the structure of a fuzzy feed-forward neural
network (FFFNN) with a trapezoidal fuzzy set. In addition to handling real input vectors, it is
also capable of handling fuzzy input vectors. Generally, the output of a FNN is a fuzzy vector.
According to the extension principle of Zadeh, each unit of a FNN has an input-output
relationship. To determine the costs associated with fuzzy calculations and fuzzy objectives,
developed a cost function. At that point, created a learning algorithm from the cost capacity to
align the four variables of each trapezoidal fuzzy weight. In conclusion, demonstrate our
methodology using numerical models.