{"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/bregman-divergence-based-approach-for","title":"Bregman Divergence Based Approach for Adaptive System Identification and Line Enhancement","arxiv_id":null,"date":"2024-03-25","proceeding":"Third International Conference on Power, Control and Computing Technologies (ICPC2T) 2024 3","authors":["Parth Sharma","Pyari Mohan Pradhan"],"abstract":"This article puts forth a novel class of least mean square (LMS) techniques, including beta divergence-inspired LMS (BLMS), Itakura-Saito divergence-inspired LMS (ISBLMS), and Kullback-Leibler divergence-inspired LMS (KLLMS). The mentioned divergence measures are part of the Bregman divergence category of information-theoretic divergence. Mean and mean-square analysis for the proffered class of algorithms is derived to find the bound on the learning-rate for stable convergence. The effectiveness of the introduced class of algorithms is showcased for the application of time-varying system identification and adaptive line enhancement.","url_abs":"https://ieeexplore.ieee.org/document/10474616","url_pdf":"https://ieeexplore.ieee.org/document/10474616","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":"bregman-divergence-based-approach-for","repo_url":"https://github.com/Parth-nXp/Bregman-Divergence-Based-Approach-for-Adaptive-System-Identification-and-Line-Enhancement","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}