NDLI: Quantized neuronal modeling: Quantum gate structure in Elman networks

Content Provider	IEEE Xplore Digital Library
Author	Penghua Li Yi Chai Qingyu Xiong
Copyright Year	2011
Description	Author affiliation: College of Automation, ChongQing University, China (Penghua Li; Yi Chai; Qingyu Xiong)
Abstract	This paper investigates the model of Elman network with quantum gate architecture and it's online learning algorithm. The new neural structure, compared with the conventional Elman network (CEN), contains a quantum map layer which can be used for solving the pattern mismatch between the context layer and the input layer. A corresponding training algorithm for this new neural architecture is also presented, as opposed to the standard back-propagation (BP) learning algorithm for ENs. According to the new learning laws, the rotation parameter and the reversal parameter of the quantum gate are updated based on gradient-descent methods. The numerical experiment shows that the proposed network has better generalization performance and faster convergence speed than conventional Elman networks.
Starting Page	315
Ending Page	320
File Size	199379
Page Count	6
File Format	PDF
ISBN	9781612843742
e-ISBN	9781612843759
DOI	10.1109/IWACI.2011.6160023
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Context Quantum computing Neurons Logic gates Vectors Biological neural networks
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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