Abstract
Fuzzy sets have been implemented efficiently to manage unclear data, language terms, andvague notions. Recently, considerable work has been dedicated to merging neural-networktechniques with fuzzy sets. In this study, present the structure of a fuzzy feed-forward neuralnetwork (FFFNN) with a trapezoidal fuzzy set. In addition to handling real input vectors, it isalso 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-outputrelationship. 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 toalign the four variables of each trapezoidal fuzzy weight. In conclusion, demonstrate ourmethodology using numerical models.
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