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The popular techniques of rule generation from numerical data such as neural networks, genetic algorithms, and fuzzy clustering are suitable when the available data pairs are large. In case of limited available data sets, a new approach for rule generation and minimization has been proposed in the present paper. Initial rules for each data pairs are generated and conflicting rules are merged based...
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