NDLI: Learning Cyclic Oscillation By Digital Type Recurrent Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Naganuma, H. Oohori, T. Watanabe, K.
Copyright Year	2006
Description	Author affiliation: Hokkaido Inst. of Technol., Hokkaido (Naganuma, H.)
Abstract	The error back propagation through time (called BPTT) is a learning method of the recurrent neural network. The network learned by BPTT can solve the dynamical problem with the time series data. However, it is not able to directly solve a digital type problem where the middle layer output is intrinsically binary such as the internal state inference of the automaton. Oohori et al. proposed a digital version of the back propagation (called DBP) for a hierarchical digital neural network consisting of the binary units. The DBP can solve a linearly nonseparable problem, and learning performance is comparable to the conventional BP. We propose a digital back propagation through time (called DBPTT) based on the DBP where the BPTT is applied to the digital type recurrent neural network. The learning of the DBPTT is fast and easy for hardware implementations. Simulation results for digital cyclic oscillation show that the performance of the DBPTT is comparable in learning and superior in generalization to the conventional BPTT.
Starting Page	3167
Ending Page	3174
File Size	290321
Page Count	8
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247300
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Neural networks Learning systems Learning automata Hardware Error correction Feedforward systems Educational institutions Design engineering Neurofeedback
Content Type	Text
Resource Type	Article

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