Abstract
Morphological neural networks (MNN) are a class of artificial neural networks whose operations are derived from mathematical morphology. In a morphological neural net, the usual sum of weighted inputs is replaced by a maximum or minimum of weighted inputs (in this context, the weighting is performed by summing the weight and the input). We speak of a max product, a min product respectively. The emphasis of this paper is on morphological associative memories (MAM), in particular on binary autoassociative morphological memories (AMM). We give a new set theoretic interpretation of recording and recall in binary AMM and provide a generalization using fuzzy set theory.
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.
