Ergen, TolgaKozat, Süleyman Serdar2018-04-122018-04-122017http://hdl.handle.net/11693/37600Date of Conference: 15-18 May 2017Conference Name: IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017In 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.TurkishDistributed particle filteringLong short term memory networkNonlinear regressionOnline learningMonte Carlo methodsRegression analysisSignal filtering and predictionState space methodsConventional methodsDistributed particlesNonlinear regression problemsNonlinear state spaceTraining algorithmsConference Paper10.1109/SIU.2017.7960220