Analysis of artificial neural network based on pq-rung orthopair fuzzy linguistic muirhead mean operators

Artificial neural network (ANN) also known simply as a neural network, is a branch of machine learning(ML), that is developed based on neuronal organization discovered by connectionism in the biological neural network in animal intelligence. In this manuscript, we invent the theory of pq-rung orthopair fuzzy linguistic (pq-ROFL) set and their valuable properties. Moreover we expose the theory of pq-ROFL Muirhead mean (pq-ROFLMM), pq-ROFL weighted Muirhead mean (pq-ROFLWMM), pq-ROFL dual Muirhead mean (pq-ROFLDMM), and pq-ROFL dual weighted Muirhead mean (pq-ROFLDWMM) operators. Some effective and reliable properties of the invented theory are also derived. Additionally, we also evaluate the unkhnown weight vector of criteria by using analytical hierarchy process (AHP). Moreover, we discovered the best type of artificial neural network under the consideration of derived operators for pq-ROFL information. Finally we illustrate some numerical examples in the environment of multi-attribute decision-making (MADM) and try to compare the proposed results with some prevailing results to show the reliability and supremacy of the invented approaches.