Techniques for the Fusion of Symbolic Rules in Distributed Organic Systems

Humans do not only learn by their own experience but also by rules obtained from other humans. It is a challenging idea to enable distributed, intelligent computer systems to follow this human archetype. A basic technique needed for such an "organic" system is the fusion of functional knowledge in form of symbolic rules that are gained from several sources (nodes of the distributed system). We assume that these nodes are equipped with self-learning classifiers on the basis of a hybrid radial basis function network/fuzzy system paradigm. We provide methods for the fusion of fuzzy-type rules extracted from such classifiers. These methods aim at preserving the consistency and comprehensibility of a found rule set (e.g. low number of rules, distinguishability of membership functions) by means of a regularization approach

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