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ETD Abstract Container

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Switching Neural Network Systems for Nonlinear Tracking

Ghimire, Manoj
http://orcid.org/0000-0001-9994-4076
http://rave.ohiolink.edu/etdc/view?acc_num=wright154708422929052

Abstract Details

2018, Master of Science in Electrical Engineering (MSEE), Wright State University, Electrical Engineering.
In this thesis, we consider the problem of tracking in complex nonlinear dynamical systems. While the Kalman filter is known to be the mean-squared error optimal tracker under linear dynamics and linear measurements, more sophisticated models and algorithms are required for complex dynamics. Here, we consider switching systems where the dynamical properties vary (''switch modes") over time. For example, the dynamics of a vehicle may switch as it transitions from interstate to urban conditions, human speech dynamics switch as speakers change, and stock market dynamics switch with discrete news events. In this work, we use mode-dependent neural networks to capture different nonlinear dynamics in a given system, and we developed a new algorithm, dubbed the Switching Neural Network Tracker (SNNT), to track modes and states over time. The proposed Bayesian system includes Markovian dynamics to model mode transitions and employes the Unscented Transform to mitigate computational complexity while estimating posterior probabilities. Examples using synthetic robot motion data and measured honeybee dance data demonstrate accurate mode identification and a dramatic reduction in state estimation error relative to non-switching systems.
Joshua Ash, Ph.D. (Advisor)
Arnab Shaw, Ph.D. (Committee Member)
Fred Garber, Ph.D. (Committee Member)
65 p.
Electrical Engineering
Switching Neural Network Systems For Nonlinear Tracking; Tracking; SNNT; Nonlinear Tracking; Unscented Kalman Filter; Bayesian System; Neural Networks; Nonlinear System; Switching Modes; Modes; States

Recommended Citations

Citations

  • Ghimire, M. (2018). Switching Neural Network Systems for Nonlinear Tracking [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright154708422929052

    APA Style (7th edition)

  • Ghimire, Manoj. Switching Neural Network Systems for Nonlinear Tracking. 2018. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright154708422929052.

    MLA Style (8th edition)

  • Ghimire, Manoj. "Switching Neural Network Systems for Nonlinear Tracking." Master's thesis, Wright State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright154708422929052

    Chicago Manual of Style (17th edition)

wright154708422929052
359
© 2018, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.