Extended Kalman Filter Taining of Neural Networks on a SIMD Parallel Mchine
The extended Kalman filter (EKF) algorithm has been shown to be advan- tageous for neural network trainings. However, unlike the backpropagation (BP), many matrix operations are needed for the EKF algorithm and therefore greatly increase the computational complexity. This paper presents a method to do the EKF training on a SIMD parallel machine. We use a multistream decoupled extended Kalman filter (DEKF) training algorithm which can provide efficient use of the parallel resource and more improved trained network weights. From the overall design consideration of the DEKF algorithm and the consideration of maximum usage of the parallel resource, the multistream DEKF training is realized on a MasPar SIMD parallel machine. The performance of the parallel DEKF training algorithm is studied. Comparisons are performed to investigate pattern and batch-form trainings for both EKF and BP training algorithms.
S. Li et al., "Extended Kalman Filter Taining of Neural Networks on a SIMD Parallel Mchine," Journal of Parallel and Distributed Computing, Elsevier Science Ltd., Apr 2002.
The definitive version is available at https://doi.org/10.1006/jpdc.2001.1807
Electrical and Computer Engineering
Keywords and Phrases
Backpropagation; Extended Kalman Filter; Massively Parallel Processing; Neural Network
International Standard Serial Number (ISSN)
Article - Journal
© 2002 Elsevier Science Ltd., All rights reserved.
01 Apr 2002