Abstract
A supervised fuzzy inference network (FIN) model and its learning algorithm for invariant pattern recognition are presented in this paper. This fuzzy inference network is suitable for 2-D visual pattern recognition problems and has been tested with letter patterns of black and white pixel values. In contrast to most of the conventional pattern recognition systems, the proposed fuzzy inference network for pattern recognition does not require any pre-processing of feature extraction. Instead, the feature extraction step is incorporated in the structure of the network. The learning speed of the proposed fuzzy inference network is fast. The structure of the proposed fuzzy inference network is simple and it performs well when applied in invariant pattern recognition problems.
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.
