NDLI: Scalable closed-boundary analog neural networks

Content Provider	IEEE Xplore Digital Library
Author	Fakhraie, S.M. Farshbaf, H. Smith, K.C.
Copyright Year	1990
Abstract	In many pattern-classification and recognition problems, separation of different swarms of class representatives is necessary. As well, in function-approximation problems, neurons with a local area of influence have demonstrated measurable success. In our previous work, we have shown how intrinsic quadratic characteristics of traditional metal-oxide-semiconductor (MOS) devices can be used to implement hyperspherical discriminating surfaces in hardware-implemented neurons. In this work, we further extend the concept from quadratic forms to more-arbitrary closed-boundary shapes. Accordingly, we demonstrate how intrinsic characteristics of submicron MOS devices can be utilized to implement efficient pattern discriminators for various applications and, through representative simulations, show their success in some typical function-approximation problems. Further, we offer two mathematical interpretations of possible roles for these networks: Geometrically, we show that our networks employ closed hypercone shapes as their discriminating surfaces; analytically, we show that a set of these synapses connected to a common integrating body calculates the distance between their inputs and weight vectors using a power norm. The feasibility of the idea is practically investigated by design, implementation, and test of a three-dimensional (3-D) closed-boundary pattern classifier, fabricated in 0.35-/spl mu/m complimentary MOS, whose results are reflected in this work.
Sponsorship	IEEE Computational Intelligence Society
Page Count	13
File Size	714362
Starting Page	492
Ending Page	504
File Format	PDF
ISSN	10459227
Volume Number	15
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-03-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Pattern recognition Neurons Shape Function approximation CMOS process Speech recognition Vectors Area measurement MOS devices
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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