Translation and Extraction Problems for Neural and Fuzzy Systems: Bridging Over Distributed Knowledge Representation in Multilayered Neural Networks and Local Knowledge Representation in Fuzzy Systems

Publisher Summary This chapter discusses the translation and extraction problems associated with the integration of qualitative and distributed knowledge representation techniques. The proposed methods bridge the gap between these two knowledge representation schemes as implemented in neural networks and in fuzzy systems. The translation mechanism helps in organizing knowledge into a neural network (NN) structure to exploit its learning capability. The extraction mechanism helps in understanding what the NN has learned from the data by extracting linguistic rules. The information contained in the NN is unreadable without extraction mechanism because of its distributed nature. The method described in this chapter makes possible a hybrid approach. Part of this approach is demonstrated in the incremental learning, where the NN is improved incrementally by adding a new piece of knowledge to the existing NN after it is translated into a NN structure. Another method is possible, whereby the NN based on the data is trained, the rule extraction algorithm to obtain information on the structure is applied, and the knowledge to reconstruct and train the NN is used. The interaction between the data-oriented learning NN and knowledge-oriented fuzzy systems promises to be a fertile topic of future research.