Analyzing industrial robot selection based on a fuzzy neural network under triangular fuzzy numbers

It is difficult to select a suitable robot for a specific purpose and production environment among the many different models available on the market. For a specific purpose in industry, a Pakistani production company needs to select the most suitable robot. In this article, we introduce a novel Triangular fuzzy neural network with Yager aggregation operator. Furthermore, the Triangular fuzzy neural network applied to the decision making model for the selection of the most suitable robot for a Pakistani production company. In this decision model, we first collect four expert information matrices in the form of Triangular fuzzy numbers about the robot for a specific purpose and production environment. After that, we calculate the criteria weights of inputs signals by using the distance measure technique. Moreover, we use the Yager aggregation operator to calculate the hidden layer information of the Triangular fuzzy neural network. Follow that, we calculate the criteria weights of hidden layer information by using the distance measure technique. Furthermore, we use the Yager aggregation operator to calculate the output layer information, and also calculate the score value of the output layer information of Triangular fuzzy neural network. Finally, we use two activation functions to calculate the output results of the Triangular fuzzy neural network and rank the output results to select the best robot for a specific purpose and production environment.