H
Howard J. Chizeck
Researcher at University of Washington
Publications - 196
Citations - 6972
Howard J. Chizeck is an academic researcher from University of Washington. The author has contributed to research in topics: Control theory & Deep brain stimulation. The author has an hindex of 41, co-authored 194 publications receiving 6487 citations. Previous affiliations of Howard J. Chizeck include Massachusetts Institute of Technology & Veterans Health Administration.
Papers
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Journal ArticleDOI
Controllability, stabilizability, and continuous-time Markovian jump linear quadratic control
Y. Ji,Howard J. Chizeck +1 more
TL;DR: In this paper, necessary and sufficient conditions for the existence of finite cost, constant, stabilizing controls for the infinite-time Markovian jump linear quadratic (JLQ) problem are established.
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Stochastic stability properties of jump linear systems
TL;DR: In this paper, the authors studied stochastic stability properties in jump linear systems and the relationship among various moment and sample path stability properties, and showed that all second moment stability properties are equivalent and are sufficient for almost sure sample path stabilisation.
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Adaptive deep brain stimulation for Parkinson's disease using motor cortex sensing.
Nicole C. Swann,Coralie de Hemptinne,Margaret C. Thompson,Svjetlana Miocinovic,Andrew M. Miller,Ro’ee Gilron,Jill L. Ostrem,Howard J. Chizeck,Philip A. Starr +8 more
TL;DR: This is the first demonstration of adaptive DBS in Parkinson's disease using a fully implanted device and neural sensing, and the approach is distinct from other strategies utilizing basal ganglia signals for feedback control.
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Discrete-time Markovian-jump linear quadratic optimal control
TL;DR: In this paper, the optimal control of discrete-time linear systems that possess randomly jumping parameters described by finite-state Markov processes is studied, and necessary and sufficient conditions for the existence of optimal constant control laws which stabilize the controlled system as the time horizon becomes infinite, with finite optimal expected cost.
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Neural network control of functional neuromuscular stimulation systems: computer simulation studies
James J. Abbas,Howard J. Chizeck +1 more
TL;DR: A neural network control system designed for the control of cyclic movements in functional neuromuscular stimulation (FNS) systems can provide automated customization of the feedforward controller parameters for a given musculoskeletal system and can resist the effects of mechanical disturbances.