Verifying fuzzy domain theories using a neural network model

In this paper, a fuzzy neural network model, named knowledge-based neural network with trapezoid fuzzy set (KBNN/TFS), that processes trapezoid fuzzy inputs is proposed. In addition to fuzzy rule revision, the model is capable of fuzzy rule verification and generation. To facilitate the processing of fuzzy information, LR-fuzzy interval is employed. Imperfect domain theories can be directly translated into KBNN/TFS structure and then revised by neural learning. A consistency checking algorithm is proposed for verifying the initial knowledge and the revised fuzzy rules. The algorithm is aimed at finding the redundant rules, conflicting rules and subsumed rules in fuzzy rule base. We show the workings of the proposed model on a knowledge base evaluator. The result show that the proposed algorithm can detect the inconsistencies in KBNN/TFS. By removing the inconsistencies and applying a rule insertion mechanism, the results are greatly improved. Besides, a consistent fuzzy rule base is obtained