Neural networks based online learning
Kozat, S. S.
2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Institute of Electrical and Electronics Engineers Inc.
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In this paper, we investigate online nonlinear regression and introduce novel algorithms based on the long short term memory (LSTM) networks. We first put the underlying architecture in a nonlinear state space form and introduce highly efficient particle filtering (PF) based updates, as well as, extended Kalman filter (EKF) based updates. Our PF based training method guarantees convergence to the optimal parameter estimation under certain assumptions. We achieve this performance with a computational complexity in the order of the first order gradient based methods by controlling the number of particles. The experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods. © 2017 IEEE.
Long short term memory network
Extended Kalman filters
Monte Carlo methods
Signal filtering and prediction
State space methods
Nonlinear state space
Optimal parameter estimation
Short term memory
Permalink (Please cite this version)http://hdl.handle.net/11693/37601
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