NDLI: Analog computation via neural networks

Content Provider	IEEE Xplore Digital Library
Author	Siegelmann, H.T. Sontag, E.D.
Copyright Year	1993
Description	Author affiliation: Rutgers Univ., New Brunswick, NJ, USA (Siegelmann, H.T.; Sontag, E.D.)
Abstract	The authors pursue a particular approach to analog computation, based on dynamical systems of the type used in neural networks research. The systems have a fixed structure, invariant in time, corresponding to an unchanging number of 'neurons'. If allowed exponential time for computation, they turn out to have unbounded power. However, under polynomial-time constraints there are limits on their capabilities, though being more powerful than Turing machines. These networks are not likely to solve polynomially-NP-hard problems, as the equality 'P=NP' implies the almost complete collapse of the standard polynomial hierarchy. In contrast to classical computational models, the models studied exhibit at least some robustness with respect to noise and implementation errors.
Starting Page	98
Ending Page	107
File Size	912494
Page Count	10
File Format	PDF
ISBN	0818636300
DOI	10.1109/ISTCS.1993.253479
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-06-07
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analog computers Computer networks Neural networks Polynomials Circuits Computational modeling Intelligent networks Turing machines Equations Mathematics
Content Type	Text
Resource Type	Article

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