Hierarchical model of decision-making based on fuzzy neural networks for information processing

Application of classical mathematical methods to solution of decision-making problems is difficult, intelligent systems are more effective for this purpose, intelligent system is the synthesis of adaptive and conventional mathematical algorithms. According to the vector approach, the problem of decision-making through the decomposition properties of alternatives is a hierarchical system of criteria. Here there is a problem of inverse transition to assessment and comparison of alternatives in general. This problem involves solution of problem composition of criteria for levels of hierarchy, which is implemented by a neural network. The problem is solved by method of nested scalar convolutions. The developed hierarchical fuzzy neural network is described.