Einstein t-Norm and t-Conorm-Based Nonlinear Diophantine Rough Fuzzy Model for fuzzy neural networking

Einstein t-Norm and t-Conorm-Based Nonlinear Diophantine Rough Fuzzy

Resumo

The approach builds a strong computational framework that can handle complicated, contradictory, and ambiguous data by combining the nonlinear Diophantine rough fuzzy set with Einstein t-norm and t-conorm-based aggregation operations. Higher expressiveness in describing uncertainty is made possible by the architecture's foundation in nonlinear Diophantine rough fuzzy sets. New operating regulations and advanced aggregation operators, such as NLDRFEWA, NLDRFEOWA, and NLDRFEHWA, are suggested in order to further strengthen the decision-making process. The decision-maker's preferences and the relative weighting of criteria are captured by the operators in a nonlinear and linguistically responsive way. Additionally, the model incorporates fuzzy neural networking (FNN) to learn intricate patterns from decision data, allowing for intelligent management of nonlinear uncertainty, adaptive weight assignment, and strong generalization. The framework makes decisions with greater flexibility and accuracy by fusing rule-based fuzzy reasoning with neural networks' capacity for learning. Additionally, advanced accuracy and scoring algorithms are used to improve alternative ranking and discriminating. The efficiency and suitability of the suggested method are demonstrated by a case study of the assessment of leading 5G network providers. The strength of the framework in producing consistent, logical, and interpretable conclusions under complex fuzzy inputs is highlighted by comparison with existing MCDM techniques. The results confirm that fuzzy neural networking, nonlinear Diophantine rough fuzzy theory, and Einstein aggregation significantly enhance the quality of decision-making in risky and uncertain scenarios. In addition to contributing to the growing corpus of fuzzy MCDM research, this work offers telecommunications and other high-risk industries a reliable decision-support tool.