Rule base reduction for knowledge-based fuzzy controllers with application to a vacuum cleaner

A rule base reduction and tuning algorithm is proposed as a design tool for the knowledge-based fuzzy control of a vacuum cleaner. Given a set of expert-based control rules in a fuzzy rule base structure, proposed algorithm computes the inconsistencies and redundancies in the overall rule set based on a newly proposed measure of equality of the individual fuzzy sets. An inconsistency and redundancy measure is proposed and computed for each rule in the rule base. Then the rules with high inconsistency and redundancy levels are removed from the fuzzy rule base without affecting the overall performance of the controller. The algorithm is successfully tested experimentally for the control of a commercial household vacuum cleaner. Experimental results demonstrate the effective use of the proposed algorithm.