Evidential reasoning neural networks

This paper proposes an neural network architecture for evidential reasoning. This has been achieved by combining an extended multilayered neural network for learning rules and decision trees with a new interpretation. The new interpretation of the decision tree converts a decision tree into an evidential reasoning construct called hierarchy tree (HT). Fuzzy knowledge representation methods have been used in the HTs for approximate reasoning. Fusing the HT into a neural network it is shown that imprecision and ignorance can be handled