Abstract
AbstractBecause of its high performance, the learning vector quantization (LVQ) proposed by Kohonen is noteworthy as a method for realizing a neural network. We propose herein a new method of LVQ using fuzzy theory and call it “fuzzy learning vector quantization” (FLVQ).FLVQ algorithm is as simple as that of LVQ, and its capability of pattern recognition is higher than that of a conventional neural network. Although it is difficult for conventional neural networks to discriminate an input pattern of an unknown category from those of known ones, FLVQ can do it. Since reference vectors of FLVQ are described by use of fuzzy sets and their membership functions are obtained from learning, the data features can be effectively extracted using FLVQ.We have used FLVQ in an odor pattern recognition system and compared its capability with those of conventional neural networks. As a result, it was confirmed that FLVQ had higher ability in an odor discrimination from the known one than is the case in conventional networks and that an unknown odor was discriminated by FLVQ.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
