Decision making on creditworthiness, using a fuzzy connectionist model

In this paper the authors present an unpolished expert system development tool, based on a connectionist architecture for knowledge representation. The present work is centered around a connectionist expert system, which can be expanded, and updated through learning of sample domain specific cases. A cell recruitment learning algorithm capable of foregetting previously learned facts by learning new ones is incorporated. Using this learning mechanism, the authors let the system learn a knowledge base on classifying the creditworthiness of credit applicants. The knowledge base consisted of 50 credit cases. It will be shown that the