NDLI: The convergent learning of three-layer artificial neural networks for any binary-to-binary mapping

Content Provider	IEEE Xplore Digital Library
Author	Kim, J.H. Sung-kwon Park In Sook Kim
Copyright Year	1994
Description	Author affiliation: Center for Adv. Comput. Studies, Southwestern Louisiana Univ., Lafayette, LA, USA (Kim, J.H.)
Abstract	In this paper, the learning algorithm called expand-and-truncate learning (ETL) is proposed to train three-layer binary neural networks (BNN) with guaranteed convergence for any binary-to-binary mapping. The most significant contribution of this paper is the development of learning algorithm for three-layer BNN which guarantees the convergence, automatically determining a required number of neurons in the hidden layer. Furthermore, the learning speed of the proposed ETL algorithm is much faster than that of backpropagation learning algorithm in a binary field. Neurons in the proposed BNN employ a hard-limiter activation function, only integer weights and integer thresholds. Therefore, this will greatly facilitate actual hardware implementation of the proposed BNN using currently available digital VLSI technology.
Starting Page	4755
Ending Page	4760
File Size	507443
Page Count	6
File Format	PDF
ISBN	078031901X
DOI	10.1109/ICNN.1994.375044
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Neurons Power line communications Neural networks Convergence Hardware Space technology Guidelines Very large scale integration
Content Type	Text
Resource Type	Article

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