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Level identification using input data mining for hierarchical fuzzy system

Wong, K.W. and Gedeon, T. (2001) Level identification using input data mining for hierarchical fuzzy system. In: The Seventh Australian and New Zealand Intelligent Information Systems Conference, 18-21 Nov. 2001, Perth, W.A pp. 379-383.

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Fuzzy rule based systems have been very popular in many control applications. However, when fuzzy control systems are used in real problems, many rules may be required. A hierarchical fuzzy system that partitions a problem for more efficient computation may be the answer. When creating a hierarchical fuzzy system, the level identification stage is crucial and time-consuming. This has a direct effect on how efficient the hierarchical fuzzy system is. This paper reports the use of an input data mining technique to efficiently perform the level identification stage. Without the use of input data mining, k*(k-1) ways of building the hierarchical fuzzy system must be tried.

Item Type: Conference Paper
Murdoch Affiliation(s): School of Information Technology
Publisher: IEEE
Copyright: © 2001 IEEE
Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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