Name
Abstract | ||
|---|---|---|
In rule-based fuzzy control systems there is a need to evaluate the rulebase to detect redundant or contradictory rules, and to monitor the performance of the knowledge acquisition process, knowledge coming either from an human expert or from a learning mechanisms if present. When faulty situations are detected corrective steps should be taken by the knowledge engineer or by a rulebase-maintenance metaknowledge. In this paper, a new ideal defuzzifier framework is presented with its use to develop matrix expressions for the consistency of the rules in a base and asessing the quality and quantity of the knowledge represented by them. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1016/b978-0-08-042236-7.50015-8 | AIRTC |
Keywords | DocType | Volume |
artificial intelligence,fuzzy set theory,learning systems,fuzzy control,knowledge engineering | Conference | 19 |
ISSN | Citations | PageRank |
Annual Review in Automatic Programming | 0 | 0.34 |
References | Authors | |
0 | 2 | |