A neural perceptron framework for tackling intuitionistic fuzzy linguistic MAGDM complexities

To introduce the Linguistic Intuitionistic Fuzzy Artificial Neural Network, a sophisticated and innovative neural network-based framework for handling Multi-Attribute Group Decision-Making (MAGDM) problems, which builds on the fundamental work of Linguistic Intuitionistic Fuzzy Sets (LIFS). The suggested model addresses hesitancy and uncertainty in choice contexts by combining linguistic intuitionistic fuzzy inputs with an Artificial Neural Network (ANN) operated by a perceptron. The proposed model integrates a Perceptron-driven ANN with linguistic intuitionistic fuzzy inputs to address uncertainty and hesitation in decision environments. Unlike earlier approaches that focused solely on fuzzy aggregation and normalization, this method embeds linguistic fuzzy representations within a trainable ANN structure. The network learns and adjusts decision weights dynamically, enhancing adaptability and ranking precision. To improve the decision-making problem, a novel Linguistic Intuitionistic Fuzzy Weighted Arithmetic Aggregation (LinIFWAA) operator and novel defuzzification functions are proposed for combining the linguistic data effectively. Lastly, the ANN model with the Perceptron learning rule specifically suggested for LIFS is also used to solve the input generated by solving the MAGDM problem. Experimental results demonstrate the model’s effectiveness in delivering interpretable and scalable solutions for complex, linguistically expressed decision-making.

کلیدواژه ها:

WA and OWA operator ، Decision support system ، Multi-attribute group decision-making ، Artificial Neural Network ، Linguistic IFS