Iterative Associative Method of Dynamic Consequents (IAMDC) for Mamdani Fuzzy Systems Type I as an Attention Mechanism
DOI:
https://doi.org/10.23055/ijietap.2025.32.6.11225Keywords:
Mamdani fuzzy systems, attention mechanism, data-driven approach, expert-driven approach, k-meansAbstract
The description of a fuzzy inference system (FIS) is qualitative, known as the expert-driven approach. Generally, antecedents are determined combinatorially, while consequents are defined based on expert knowledge, which often involves issues of high interpretability and imprecision. Therefore, this article presents an Iterative Associative Method of Dynamic Consequents (IAMDC) as an Attention Mechanism. The k-means algorithm is used to group premises based on the distance-similarity of their fuzzy values, in numerical and objective form, thereby eliminating the imprecision of experts in defining the consequents' categories, making a significant contribution to the development of FIS. Furthermore, based on this distance, the consequents of each rule are classified. A normalization phase is proposed for the distances obtained in each rule to identify the most significant probability of occurrence, and based on this, estimate the parameters of the consequences in each rule using the corresponding product fuzzy operator, which is another significant contribution of this research. The novel prototype is validated through a simulation model based on the case of an automotive manufacturing company, in which supplier evaluation is developed using four evaluation criteria. Five possible combinations—Prod-Max, Min-Max, Max-Min, Max-Max, and Max-Prod — were used as inference rules for the proposed associative fuzzy inference system (AFIS) and compared with the present evaluation method in the company and with a conventional fuzzy inference system. The results of the proposed system were more accurate and reliable, with lower mean-squared error values.
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