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<p>This study proposes a novel fuzzy neural network (FNN) framework operating under complex Fermatean fuzzy sets to enhance multiple attribute decision-making (MADM) in uncertain environments. The model integrates Schweizer–Sklar-based aggregation operators within the FNN’s computational layers to process complex fuzzy information, capturing both membership and non-membership degrees. Each layer of the network handles CFF data, enabling adaptive learning and maintaining interpretability. A case study on evaluating innovation in informatization stages demonstrates the model's effectiveness. Results show that the proposed CFNN approach delivers consistent and reliable rankings, reduces subjectivity, and outperforms traditional MADM methods in handling ambiguity and uncertainty.</p>

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Multiple attribute decision-making based on fuzzy neural network under complex Fermatean fuzzy data

  • 期刊论文

  • 发表日期：

    2025年9月22日

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  • Aliya Fahmi,
  • Saeed Islam,
  • Muhammad Arshad Shehzad Hassan,
  • Ishtiaq Ali

摘要

This study proposes a novel fuzzy neural network (FNN) framework operating under complex Fermatean fuzzy sets to enhance multiple attribute decision-making (MADM) in uncertain environments. The model integrates Schweizer–Sklar-based aggregation operators within the FNN’s computational layers to process complex fuzzy information, capturing both membership and non-membership degrees. Each layer of the network handles CFF data, enabling adaptive learning and maintaining interpretability. A case study on evaluating innovation in informatization stages demonstrates the model's effectiveness. Results show that the proposed CFNN approach delivers consistent and reliable rankings, reduces subjectivity, and outperforms traditional MADM methods in handling ambiguity and uncertainty.