Neural explicit and implicit knowledge representation

A unified approach for integrating explicit and implicit knowledge in connectionist knowledge-based systems is proposed. The explicit knowledge is represented by discrete fuzzy rules, which are directly mapped into an equivalent Multi Purpose Neural Network based on MAPI neuron. Some methods based upon interactive fuzzy operators are presented in order to extract fuzzy rules from trained neural networks. Architecture for a neural knowledge-based system is proposed as a combination of modules based on data learning and fuzzy rules mapping. The combination of explicit and implicit knowledge modules is viewed as an iterative process in knowledge acquisition and refinement.