A Neuropsychology-inspired Learning System for Human Uncertainty Monitoring

Abstract

Uncertainty exists in various complex problems. Yet, human is able to effectively handle these uncertainties and makes appropriate decision. Thus, modeling of human uncertainty process should improve the performance of learning system in uncertain environment. A mechanism for human uncertainty monitoring is the broad and narrow generalization in category learning. This can be modeled using upper and lower membership functions, which corresponds to the broad and narrow generalizations respectively. These upper and lower membership functions can be implemented using the fuzzy rough set (FR) theory. A complementary learning fuzzy neural network (CLFNN) is a functional model of human pattern recognition. It is integrated with the human uncertainty monitoring model and the resultant FRCLFNN offers good classification performance and better representation power as it captures input, linguistic, and rough uncertainties. Experimental result supports that FRCLFNN is a competent decision support system