{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-extended-neo-fuzzy-neuron-and-its-adaptive","title":"An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm","arxiv_id":"1610.06483","date":"2016-10-20","proceeding":null,"authors":["Yevgeniy V. Bodyanskiy","Oleksii K. Tyshchenko","Daria S. Kopaliani"],"abstract":"A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy\nneuron (ENFN)) that is characterized by improved approximating properties. An\nadaptive learning algorithm is proposed that has both tracking and smoothing\nproperties. An ENFN distinctive feature is its computational simplicity\ncompared to other artificial neural networks and neuro-fuzzy systems.","url_abs":"http://arxiv.org/abs/1610.06483v1","url_pdf":"http://arxiv.org/pdf/1610.06483v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-extended-neo-fuzzy-neuron-and-its-adaptive","repo_url":"https://github.com/Amir-Samadi/extended-neo-fuzzy-neuron-ENFN-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}