Pattern classification by distributed representation of fuzzy rules

Abstract

The authors introduce the concept of distributed representation of fuzzy rules and apply it to classification problems. Distributed representation is implemented by superimposing many fuzzy rules corresponding to different fuzzy partitions of a pattern space. This means that many fuzzy rule tables are simultaneously employed, corresponding to different fuzzy partitions in fuzzy inference. To apply distributed representation of fuzzy rules to pattern classification problems, the authors first propose an algorithm to generate fuzzy rules from numerical data. Next they propose a fuzzy inference method using the generated fuzzy rules. The classification power of distributed representation was compared with that of ordinary fuzzy rules which can be viewed as a local representation.<>