A decision personal index of fuzzy numbers based on neural networks

In a previous work, the authors showed that artificial neural networks (ANNs) could learn the criteria for comparing fuzzy numbers of a real decision maker. A multilayer feed-forward ANN and the backpropagation algorithm, and trapezoidal fuzzy numbers were considered. The criteria of three people were learnt with an ANN. The trained ANN is considered as a personal method of the decision-maker to compare fuzzy utilities and it has been applied to some decision problems. In this paper, a decision personal index (DPI) of fuzzy numbers based on the trained ANN is developed in order to measure distance between the numbers. The DPI ranks them with values in [0,1] interval, and we apply it to some problems on matrix games and linear optimization with a fuzzy environment. Some examples are also shown.