A course in fuzzy systems and control
A course in fuzzy systems and control
Constructive theory for fuzzy systems
Fuzzy Sets and Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
An Incremental Learning Algorithm for Constructing Decision Rules
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
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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.