NDLI: A novel fast learning algorithms for time-delay neural networks

Content Provider	IEEE Xplore Digital Library
Author	Jiang Minghu Zhu Xiaoyan
Copyright Year	1999
Description	Author affiliation: Dept. of Comput., Tsinghua Univ., Beijing, China (Jiang Minghu)
Abstract	To counter the drawbacks of long training time required by Waibel's time-delay neural networks (TDNN) in phoneme recognition, the paper puts forward several improved fast learning methods for TDNN. Merging the unsupervised Oja rule and the similar error backpropagation algorithm for initial training of TDNN weights can effectively increase the convergence speed. Improving the error energy function and updating the changing of weights according to size of output error, can increase the training speed. From backpropagation along layer, to average overlap part of backpropagation error of the first hidden layer along a frame, the training samples gradually increase the convergence speed increases. For multi-class phonemic modular TDNNs, we improve the architecture of Waibel's modular networks, and obtain an optimum modular TDNNs of tree structure to accelerate its learning. Its training time is less than Waibel's modular TDNNs.
Starting Page	1380
Ending Page	1383
File Size	389482
Page Count	4
File Format	PDF
ISBN	0780355296
ISSN	10987576
DOI	10.1109/IJCNN.1999.831164
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Convergence IEEE members Intelligent systems Intelligent networks Computer networks Counting circuits Learning systems Merging Computer errors
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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