Titlebook: Stochastic Lagrangian Adaptation; David Levanony,Peter E. Caines Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusiv

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https://doi.org/10.1007/978-3-031-73758-9Stochastic Systems; Adaptive Control; Optimization; Stochastic Lagrangian Adaptation; Probability Theory

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Asymptotic Maximum Likelihood Identification,e following strong properties: (i) AML estimates converge to finite limits; (ii) AML limits lie in specified limit sets of parameters giving rise to indistinguishable closed-loop dynamics; (iii) a particular family of AML estimates is shown to drive the associated log-likelihood gradient to zero exponentially fast.

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David Levanony,Peter E. Cainesssful of conditions. Drawing on literature from the United States (USA), this chapter reviews the culture, demographics and changing mission of the fire service as a backdrop to workplace harassment and bullying issues. The fire service has unique organizational dynamics that can lead to harassment

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David Levanony,Peter E. Cainesomes (SOLO) taxonomy. It investigates the correlation between the domain knowledge model and the SOLO taxonomy, offering practical instances of learning tasks aligned with each SOLO level. The “Overview” section introduces the chapter’s purpose, emphasizing the significance of aligning learning acti

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Introduction,ol is highlighted together with the fact that in some of its principal forms it requires consistent parameter estimation. Since sample-path Persistent Excitation (PE) of the associated regression vector process ensures consistency, various attempts to secure that property are discussed. The PE requi

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Asymptotic Maximum Likelihood Identification,e following strong properties: (i) AML estimates converge to finite limits; (ii) AML limits lie in specified limit sets of parameters giving rise to indistinguishable closed-loop dynamics; (iii) a particular family of AML estimates is shown to drive the associated log-likelihood gradient to zero exp

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