On the weight convergence of Elman networks

IEEE Transactions on Neural Networks
Qing Song

Abstract

An Elman network (EN) can be viewed as a feedforward (FF) neural network with an additional set of inputs from the context layer (feedback from the hidden layer). Therefore, instead of the offline backpropagation-through-time (BPTT) algorithm, a standard online (real-time) backpropagation (BP) algorithm, usually called Elman BP (EBP), can be applied for EN training for discrete-time sequence predictions. However, the standard BP training algorithm is not the most suitable for ENs. A low learning rate can improve the training of ENs but can also result in very slow convergence speeds and poor generalization performance, whereas a high learning rate can lead to unstable training in terms of weight divergence. Therefore, an optimal or suboptimal tradeoff between training speed and weight convergence with good generalization capability is desired for ENs. This paper develops a robust extended EBP (eEBP) training algorithm for ENs with a new adaptive dead zone scheme based on eEBP training concepts. The adaptive learning rate and adaptive dead zone optimize the training of ENs for each individual output and improve the generalization performance of the eEBP training. In particular, for the proposed eEBP training algorithm, convergen...Continue Reading

References

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Citations

May 22, 2013·Australasian Physical & Engineering Sciences in Medicine·Balasubramanian Gopinath, Natesan Shanthi
Jul 24, 2014·TheScientificWorldJournal·Shao JieReza Malekian
Feb 2, 2011·ISA Transactions·Vincent A Akpan, George D Hassapis
Aug 10, 2011·IEEE Transactions on Neural Networks·Jie LianPeng Shi
Sep 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Xiaoming LiangZonghua Liu
Mar 16, 2011·IEEE Transactions on Neural Networks·Xiaoyu Wang, Yong Huang
Oct 4, 2011·IEEE Transactions on Neural Networks·Qing Song
Feb 19, 2016·IEEE Transactions on Neural Networks and Learning Systems·Qing SongDanwei Wang

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