NDLI: Second Order Diagonal Recurrent Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Kazemy, A. Hosseini, S.A. Farrokhi, M.
Copyright Year	2007
Description	Author affiliation: Iran Univ. of Sci. & Technol., Tehran (Kazemy, A.; Hosseini, S.A.; Farrokhi, M.)
Abstract	In this paper a new diagonal recurrent neural network that contains two recurrent weights in hidden layer is proposed. Since diagonal recurrent neural networks have simpler structure than the fully connected recurrent neural networks, they are easier to use in real-time applications. On the other hand, all diagonal recurrent neural networks in literature use one recurrent weight in hidden neurons, while the proposed network takes advantage of two recurrent weights. It will be shown, by simulations, that the proposed network can approximate nonlinear functions better than the existing diagonal recurrent neural networks. After deriving the training algorithm, the convergence stability and adaptive learning rate will be presented. The performance of the proposed network in model identification shows the accuracy of this network against the diagonal recurrent neural networks. Moreover, this network will be applied to realtime control of an image stabilization platform.
Starting Page	251
Ending Page	256
File Size	562094
Page Count	6
File Format	PDF
ISBN	9781424407545
DOI	10.1109/ISIE.2007.4374607
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-04
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Neurons Neural networks Backpropagation algorithms Convergence Control systems Nonlinear control systems Nonlinear systems Fuzzy control Delay
Content Type	Text
Resource Type	Article

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