Abstract
Most of fuzzy systems use the complete combination rule set based on partitions to discover the fuzzy rules, thus often resulting in low capability of generalization and high computational complexity. To large extent, the reason originates from the fact that such fuzzy systems do not utilize the field knowledge contained in data. In this paper, based on rough set theory, a new generalized incremental rule extraction algorithm (GIREA) is presented to extract rough domain knowledge, namely, certain and possible rules. Then, fuzzy neural network FNN is used to refine the obtained rules and further produce the fuzzy rule set. Our approach and experimental results demonstrate the superiority in both rule's length and the number of fuzzy rules.
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