Using Fuzzy Feed-Forward Neural Network for Linguistic Processing - AbdulRahim K. Rah; Sozan S. Haydar (Academic Science Journal)
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.