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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Online distributed nonlinear regression via neural networks

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      Author
      Ergen, Tolga
      Kozat, Süleyman Serdar
      Date
      2017
      Source Title
      Proceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017
      Publisher
      IEEE
      Language
      Turkish
      Type
      Conference Paper
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      Abstract
      In this paper, we study the nonlinear regression problem in a network of nodes and introduce long short term memory (LSTM) based algorithms. In order to learn the parameters of the LSTM architecture in an online manner, we put the LSTM equations into a nonlinear state space form and then introduce our distributed particle filtering (DPF) based training algorithm. Our training algorithm asymptotically achieves the optimal training performance. In our simulations, we illustrate the performance improvement achieved by the introduced algorithm with respect to the conventional methods.
      Keywords
      Distributed particle filtering
      Long short term memory network
      Nonlinear regression
      Online learning
      Monte Carlo methods
      Regression analysis
      Signal filtering and prediction
      State space methods
      Conventional methods
      Distributed particles
      Nonlinear regression problems
      Nonlinear state space
      Training algorithms
      Permalink
      http://hdl.handle.net/11693/37600
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2017.7960220
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      • Department of Electrical and Electronics Engineering 3597
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