NDLI: Learning multiple-valued logic networks based on backpropagation

Content Provider	IEEE Xplore Digital Library
Author	Zheng Tang Ishizuka, O. Tanno, K.
Copyright Year	1995
Description	Author affiliation: Fac. of Eng., Miyazaki Univ., Japan (Zheng Tang; Ishizuka, O.; Tanno, K.)
Abstract	This paper describes a learning multiple-valued logic (MVL) network based on back propagation. The learning MVL network is derived directly from a canonical realization of MVL functions and therefore its functional completeness is guaranteed. We extend traditional back propagation to include the prior human knowledge on the MVL networks, for example, the architecture and the number of hidden units and layers. The prior knowledge from the MVL canonical form can be used as initial parameters of the learning MVL network in its learning process. As a result, the prior knowledge can guide the back propagation learning process to get started from a point in the parameter space that is not far from the optimal one, thus, back propagation can fine-tune the prior knowledge for achieving a desired output. This cooperative relation between the prior knowledge and the back propagation learning process is not always present in neural networks. Simulation results are also given to confirm the effectiveness of the methods.
Starting Page	270
Ending Page	275
File Size	387208
Page Count	6
File Format	PDF
ISBN	0818671181
ISSN	0195623X
DOI	10.1109/ISMVL.1995.513542
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-05-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Logic Neural networks Humans Image processing Speech recognition Feedforward systems
Content Type	Text
Resource Type	Article
Subject	Mathematics Computer Science

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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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