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|Title:||A robust recurrent simultaneous perturbation stochastic approximation training algorithm for recurrent neural networks||Authors:||Song, Qing
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Electric power||Issue Date:||2014||Source:||Xu, Z., Song, Q., & Wang, D. (2014). A robust recurrent simultaneous perturbation stochastic approximation training algorithm for recurrent neural networks. Neural Computing and Applications, 24(7-8). 1851-1866.||Series/Report no.:||Neural computing and applications||Abstract:||Training of recurrent neural networks (RNNs) introduces considerable computational complexities due to the need for gradient evaluations. How to get fast convergence speed and low computational complexity remains a challenging and open topic. Besides, the transient response of learning process of RNNs is a critical issue, especially for online applications. Conventional RNN training algorithms such as the backpropagation through time and real-time recurrent learning have not adequately satisfied these requirements because they often suffer from slow convergence speed. If a large learning rate is chosen to improve performance, the training process may become unstable in terms of weight divergence. In this paper, a novel training algorithm of RNN, named robust recurrent simultaneous perturbation stochastic approximation (RRSPSA), is developed with a specially designed recurrent hybrid adaptive parameter and adaptive learning rates. RRSPSA is a powerful novel twin-engine simultaneous perturbation stochastic approximation (SPSA) type of RNN training algorithm. It utilizes three specially designed adaptive parameters to maximize training speed for a recurrent training signal while exhibiting certain weight convergence properties with only two objective function measurements as the original SPSA algorithm. The RRSPSA is proved with guaranteed weight convergence and system stability in the sense of Lyapunov function. Computer simulations were carried out to demonstrate applicability of the theoretical results.||URI:||https://hdl.handle.net/10356/105067
|DOI:||10.1007/s00521-013-1436-5||Rights:||© 2014 Springer-Verlag London Limited. This is the author created version of a work that has been peer reviewed and accepted for publication by Neural Computing and Applications, Springer-Verlag London Limited. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1007/s00521-013-1436-5].||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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