NDLI: A robust extended Elman backpropagation algorithm

Content Provider	IEEE Xplore Digital Library
Author	Qing Song Yeng Chai Soh Lei Zhao
Copyright Year	2009
Description	Author affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 (Qing Song; Yeng Chai Soh; Lei Zhao)
Abstract	Elman networks (ENs) can be viewed as a feedforward (FF) neural network with an additional set of inputs from the context layer input (feedback from the hidden layer). Therefore, a standard on-line (real time) backpropagation (BP) algorithm, instead of the off-line backpropagation through time (BPTT) algorithm, can be applied for the training of ENs, which is usually called Elman backpropagation (EBP) for discrete time sequence prediction applications. However, the standard BP training algorithm is not the most suitable one for ENs. Using a small learning rate may help improve the training of ENs, but it can result in very slow convergence speed and poor generalization performance, while a large learning rate may lead to unstable training in terms of weight divergence. Therefore, an optimal trade-off between ENs training speed and weight convergence with good generalization capability is desired. In this paper, a robust extended Elman backpropagation (eEBP) training algorithm of ENs with a nonlinear adaptive dead zone scheme is developed based on a novel training concept. The optimized adaptive learning rate with the adaptive dead zone maximizes the training speed of the ENs for each weight updating step while generalization performance of the eEBP training is improved. Computer simulations are carried out to show the improved performance of eEBP for discrete-time sequence prediction.
Starting Page	2971
Ending Page	2978
File Size	1600828
Page Count	8
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178829
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Backpropagation algorithms Neural networks Humans Optical computing Testing Elbow Image motion analysis Legged locomotion Image segmentation
Content Type	Text
Resource Type	Article

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