Refinement of fuzzy production rules by neuro-fuzzy networks

The knowledge acquisition bottleneck is well-known in the development of fuzzy knowledge based systems (i.e. FKBSs), and knowledge maintenance and refinement are important issues. The paper improves fuzzy production rule (FPR) representation power by exploiting prior knowledge and develops refinement tools which assist in debugging a FKBS´s knowledge, thus easing the knowledge acquisition and maintenance bottlenecks. We focus on knowledge refinement where the FKBS´s knowledge is debugged or updated in reaction to evidence that the FKBS is faulty or out-of-date. Some of the applied methods are presented. To select a feasible fuzzy rule set for classification, the most difficult task is finding a set of rules pertaining to the specific classification by choosing adaptive knowledge representation parameters such as local and global weights in fuzzy rules. We map the weighted fuzzy rules to a new neural network (five-layer-based knowledge neural network) so the knowledge representation parameters can be refined and fuzzy rule representation power can be improved. The dynamic assigning neuron method, gradient-descent method with penalizing functions and evolving strategy are considered. We show that this refinement method can maintain the accuracy and improve the comprehensibility and representation power of FPRs. Experiments on a special domain indicate that the refinement method and evolving strategy are able to significantly increase an FPR´s representation power when compared with standard fuzzy knowledge-based networks