Title :
Gaussian Sum Filters for Recurrent Neural Networks training
Author :
Todorovic, Branimir ; Stankovic, Miomir ; Moraga, Claudio
Author_Institution :
Fac. of Occupational Safety, Nis Univ.
Abstract :
We consider the problem of recurrent neural network training as a Bayesian state estimation. The proposed algorithm uses Gaussian sum filter for nonlinear, non-Gaussian estimation of network outputs and synaptic weights. The performances of the proposed algorithm and other Bayesian filters are compared in noisy chaotic time series long-term prediction
Keywords :
Bayes methods; Gaussian processes; learning (artificial intelligence); recurrent neural nets; time series; Bayesian state estimation; Gaussian sum filters; noisy chaotic time series; recurrent neural networks training; Bayesian methods; Chaos; Filters; Neural networks; Neurons; Nonlinear dynamical systems; Nonlinear equations; Probability density function; Recurrent neural networks; State estimation; Gaussian sum filter; Recurrent neural networks; divided difference filter; extended Kalman filter; sequential Bayesian estimation; unscented Kalman filter;
Conference_Titel :
Neural Network Applications in Electrical Engineering, 2006. NEUREL 2006. 8th Seminar on
Conference_Location :
Belgrade, Serbia & Montenegro
Print_ISBN :
1-4244-0433-9
Electronic_ISBN :
1-4244-0433-9
DOI :
10.1109/NEUREL.2006.341175
