Abstract
This paper is devoted to the problem of fuzzy pattern recognition. The most universal case, when both images and clusters are fuzzy sets, is considered. Based on the features of level sets, an idea of linearly separable fuzzy clusters is introduced. An algorithm is proposed for deriving a decision-making function, based on the technique originally used for the crisp case. By solving a single system of linear inequations, it allows one to derive the borders of a number of level sets of clusters. These borders, being decision functions for each level respectively, at the same time produce matching functions for fuzzy clusters. All algorithms are computer-oriented and can be implemented for the automatic recognition of fuzzy patterns.
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.
