A Novel Framework for Deep Neural Network Selection Based on Bipolar Fuzzy Power Weighted Aggregation Operators

Authors

  • Sanam Ayub Department of Mathematics, Riphah International University, Islamabad, Pakistan Author
  • Ijaz ur Rahman School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China Author

Keywords:

Bipolar fuzzy sets, bipolar fuzzy aggregation, Decision making problem, Deep neural network

Abstract

Bipolar fuzzy numbers are very suitable extension of fuzzy set theory and bipolar fuzzy number illustrate the uncertainty and vagueness. By applying the bipolar fuzzy numbers to a power aggregation operator to develop a bipolar fuzzy power aggregation operator and discuss their properties. We define distance measure between two bipolar fuzzy sets. We discuss the different properties of bipolar fuzzy aggregation operator. By using the bipolar fuzzy aggregation operator, we develop an algorithm for group decision making. A numerical example explains the proposed model of group decision making problem based on bipolar fuzzy aggregation operator. The proposed model apply to select the best deep neural network. 

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Published

2025-12-24

Data Availability Statement

There is no associate data with this research.

Issue

Vol. 1 No. 01 (2025):

Section

Articles

License

Copyright (c) 2025 Journal of Fuzzy Intelligence

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


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How to Cite

A Novel Framework for Deep Neural Network Selection Based on Bipolar Fuzzy Power Weighted Aggregation Operators. (2025). Journal of Fuzzy Intelligence, 1(01), 20-30. https://mathfuzzyjournal.com/index.php/JFI/article/view/13