NDLI: A global-local hybrid Evolutionary Strategy (ES) for Recurrent Neural Networks (RNNs) in system identification

Content Provider	IEEE Xplore Digital Library
Author	Teoh, E.J. Xiang, C.
Copyright Year	2007
Description	Author affiliation: Nat. Univ. of Singapore, Singapore (Teoh, E.J.; Xiang, C.)
Abstract	Recurrent neural networks, through their unconstrained synaptic connectivity and resulting state-dependent nonlinear dynamics, offer a greater level of computational ability when compared with regular feedforward neural network (FFNs) architectures. A necessary consequence of this increased capability is a higher degree of complexity, which in turn leads to gradient-based learning algorithms for RNNs being more likely to be trapped in local optima, thus resulting in sub- optimal solutions. This motivates the use of evolutionary computational methods which center about the use of population- based global-search techniques as an optimization scheme. In this article, we propose the use of a hybrid evolutionary strategy (ES) approach together with an adaptive linear observer, acting as a local search operator, as a learning mechanism for general RNN applications. Illustrative examples, though largely preliminary in nature, in solving a few system identification problems, are encouraging.
Starting Page	1628
Ending Page	1635
File Size	178242
Page Count	8
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424668
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks System identification Neural networks Neurons Network topology Feedforward neural networks Biological neural networks Fuzzy control Control system synthesis Adaptive systems
Content Type	Text
Resource Type	Article

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