Softmax Function Based Neutrosophic Aggregation Operators and Application in Multi-Attribute Decision Making Problem

Abstract

Softmax function is a well-known generalization of the logistic function. It has been extensively used in various probabilistic classification methods such as softmax regression, linear discriminant analysis, naive bayes classifiers, and artificial neural networks. Inspired by the advantages of softmax function, we focused on this paper is to develop the softmax function based single valued neutrosophic aggregation operators. Then we have established some essential properties of aggregation operators based on softmax function with neutrosophic set. Additionally, we have defined a multi-attribute decision making method based on the proposed aggregation operators. Using this MADM method we exercised a realistic MADM problem with neutrosophic informations. Finally, we checked the validity and reliability of the proposed methods considering by one numerical illustration.

Automating Fault Test Cases Generation and Execution for Automotive Safety Validation via NLP and HIL Simulation

“…The output layer, which uses a softmax classifier, is the last layer. The softmax function compresses a K-dimensional vector of arbitrary real values into a K-dimensional probability vector where each element's value is in the range of (0,1) and the element sum is "1" [83].…”