An advanced fuzzy neural network model for nanomaterial selection in aerospace application

A mission to explore a distant planet is being planned by the Pakistan Space and Upper Atmosphere Research Commission. For this mission to be successful, they must select the most suitable nanomaterials for various parts of the spacecraft, including the hull, solar panels, and energy storage systems. For this evaluation, we propose a novel decision making model based on a fuzzy neural network under the fuzzy credibility information to select the optimal nanomaterial for these parts. To do this, we collect the information about the nanomaterials in spacecraft from three specialists. The hidden layer of the fuzzy neural network is calculated by combine the input data with their corresponding weights using the Frank aggregation operator. The importance of each criterion is determined by experts using the Shannon entropy method. The Criteria Importance through Intercriteria Correlation method is then applied to calculate the hidden layer weights, and these weights are used again with the Frank aggregation operator to combine the hidden layer information. Next, we calculate the output scores and apply activation functions to get the final results of the fuzzy neural network. We also check how sensitive the model is to changes in the Frank parameter and compare the results of our proposed model with other existing decision-making models. The comparison demonstrates that the proposed approach is useful and reliable as a decision support system.