Entropy is a significant mathematical instrument for determining ambiguous/fuzzy information. Entropy is indispensable for measuring ambiguity, first familiarized by Shannon (Syst Tech J 27:379–423, 1948) to extent the degree of uncertainty in likelihood distributions. Complex info processes are extensively pragmatic in decision-making processes. Created as per the notion of an exponential exponent for the fuzzy set, our exertion offers a measure of the power of an intuitive set of exponents. This article identifies a new measure of the exponential theorem on an intuitive set of equations. In addition, the necessary properties are displayed. By analyzing the results of the examples, it has been shown that this method is faster and more effective in practice.

熵是确定模糊/模糊信息的重要数学工具。熵是测量歧义性必不可少的方法，首先由Shannon熟悉（Syst Tech J 27：379–423，1948年），以扩大似然性分布中的不确定性程度。复杂的信息流程在决策流程中非常实用。根据模糊集的指数指数的概念创建的，我们的工作量提供了直观指数集的功效的度量。本文在直观的方程组上确定了指数定理的一种新度量。此外，还将显示必要的属性。通过分析示例的结果，已表明该方法在实践中更快，更有效。