Scholarlink

<p>In this paper, we introduce a new type of fuzzy set called Type-2 Fermatean Fuzzy Set (T2FFS), which is designed to handle more complex and uncertain information than traditional fuzzy sets since T2FFS allows the membership value itself to be fuzzy. Full theoretical framework for T2FFS, including the mathematical formulas and operators that define how they work has been developed, and provide proofs to support them. Also propose a new distance measure for comparing fuzzy sets, a new entropy measure to evaluate the level of uncertainty in the data, a score function and an accuracy function that help determine which option performs better under the given criteria. These tools make T2FFS more useful in real-life problems. To show the practical value of our new variant of fuzzy sets, we apply it to two important areas. First, we analyze India’s Gross Domestic Product (GDP), where T2FFS helps deal with uncertain and complex economic data more effectively, also perform a comparative analysis with existing methodologies using our data to demonstrate the effectiveness and reliability of our approach. Second, we use T2FFS in pattern recognition, where it improves the ability to identify and classify patterns in data with ambiguity. Our results show that Type-2 Fermatean Fuzzy Sets can provide better accuracy and understanding in situations with high uncertainty.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A study on type-2 fermatean fuzzy sets and its application to pattern recognition and assessment of factors contributing to the GDP of india

  • 期刊论文

  • 发表日期：

    2025年6月6日

查看全文
  • Mausumi Sen,
  • Shivangi Tripathi,
  • Mrinmay Pathak

摘要

In this paper, we introduce a new type of fuzzy set called Type-2 Fermatean Fuzzy Set (T2FFS), which is designed to handle more complex and uncertain information than traditional fuzzy sets since T2FFS allows the membership value itself to be fuzzy. Full theoretical framework for T2FFS, including the mathematical formulas and operators that define how they work has been developed, and provide proofs to support them. Also propose a new distance measure for comparing fuzzy sets, a new entropy measure to evaluate the level of uncertainty in the data, a score function and an accuracy function that help determine which option performs better under the given criteria. These tools make T2FFS more useful in real-life problems. To show the practical value of our new variant of fuzzy sets, we apply it to two important areas. First, we analyze India’s Gross Domestic Product (GDP), where T2FFS helps deal with uncertain and complex economic data more effectively, also perform a comparative analysis with existing methodologies using our data to demonstrate the effectiveness and reliability of our approach. Second, we use T2FFS in pattern recognition, where it improves the ability to identify and classify patterns in data with ambiguity. Our results show that Type-2 Fermatean Fuzzy Sets can provide better accuracy and understanding in situations with high uncertainty.