Abstract
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 a MAPI neuron. Some methods based upon interactive fuzzy operators are presented in order to extract fuzzy rules from trained neural networks. An 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.
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