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Optimal Input Signal Design for Data-Centric Identification and Control with Applications to Behavioral Health and Medicine


Abstract Increasing interest in individualized treatment strategies for prevention and treatment of health disorders has created a new application domain for dynamic modeling and control. Standard population-level clinical trials, while useful, are not the most suitable vehicle for understanding the dynamics of dosage changes to patient response. A secondary analysis of intensive longitudinal data from a naltrexone intervention for fibromyalgia examined in this dissertation shows the promise of system identification and control. This includes datacentric identification methods such as Model-on-Demand, which are attractive techniques for estimating nonlinear dynamical systems from noisy data. These methods rely on generating a local function approxim... (more)
Created Date 2014
Contributor Deshpande, Sunil (Author) / Rivera, Daniel E. (Advisor) / Peet, Matthew M. (Committee member) / Si, Jennie (Committee member) / Tsakalis, Konstantinos S. (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Engineering / Mathematics / Control systems / Input signal design / Model predictive control / Optimization / Polynomial optimization / System identification
Type Doctoral Dissertation
Extent 251 pages
Language English
Copyright
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Note Ph.D. Electrical Engineering 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis