The auxiliary model based hierarchical stochastic gradient algorithms and convergence analysis for feedback nonlinear systems using the multi-innovation identification theory

The auxiliary model based hierarchical stochastic gradient algorithms and convergence analysis for feedback nonlinear systems using the multi-innovation identification theory | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 January 2025 V1 Latest version Share on The auxiliary model based hierarchical stochastic gradient algorithms and convergence analysis for feedback nonlinear systems using the multi-innovation identification theory Authors : Guangqin Miao 0000-0003-1280-1254 , Dan Yang 0000-0001-6518-9025 , Feiyan Chen 0000-0001-7405-6910 , and Feng Ding 0000-0002-2721-2025 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173771120.08367883/v1 Published Journal of the Franklin Institute Version of record Peer review timeline 178 views 91 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The feedback nonlinear output-error system is a special nonlinear system, the existence of the memoryless nonlinear block on the feedback channel leads to the difficulty of the parameter estimation. Combining the hierarchical identification principle with the auxiliary model identification idea, we derive an auxiliary model-based hierarchical stochastic gradient algorithm. In order to further improve the convergence rate and parameter estimation accuracy, an auxiliary model-based hierarchical multi-innovation stochastic gradient algorithm is proposed by using the multi-innovation identification theory. Furthermore, the convergence properties of the proposed algorithms are analyzed through the stochastic process theory. Finally, the experimental results indicate the effectiveness of the proposed algorithms. Supplementary Material File (y_feedbacknonlinearam-hsg_am-hmisg_rnc-2025-1-23.pdf) Download 230.12 KB Information & Authors Information Version history V1 Version 1 24 January 2025 Peer review timeline Published Journal of the Franklin Institute Version of Record 1 May 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords auxiliary model feedback nonlinear systems gradient search hierarchical identification principle multi-innovation identification theory Authors Affiliations Guangqin Miao 0000-0003-1280-1254 no affiliation View all articles by this author Dan Yang 0000-0001-6518-9025 no affiliation View all articles by this author Feiyan Chen 0000-0001-7405-6910 no affiliation View all articles by this author Feng Ding 0000-0002-2721-2025 [email protected] no affiliation View all articles by this author Metrics & Citations Metrics Article Usage 178 views 91 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Guangqin Miao, Dan Yang, Feiyan Chen, et al. The auxiliary model based hierarchical stochastic gradient algorithms and convergence analysis for feedback nonlinear systems using the multi-innovation identification theory. Authorea . 24 January 2025. 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