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Proceedings Paper

Behavioral circuit modeling using neural networks
Author(s): Stephen V. Kosonocky
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Paper Abstract

A technique is presented for automatic generation of Analog Behavioral circuit models using feed-forward neural networks in static and dynamic configurations. These models are generated, by using the data output from an accurate SPICE simulation to train a neural network to model a particular circuit function. Results are given using two types of neural networks, a static neural network to model an analog multiplier, and a recurrent neural network for modeling the dynamics of a bandlimited circuit. Simulations show that neural networks are able to learn the essential nonlinear and dynamic properties found in these circuits using the training technique described.

Paper Details

Date Published: 1 February 1994
PDF: 12 pages
Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); doi: 10.1117/12.172495
Show Author Affiliations
Stephen V. Kosonocky, Rutgers Univ. (United States)


Published in SPIE Proceedings Vol. 2093:
Substance Identification Analytics
James L. Flanagan; Richard J. Mammone; Albert E. Brandenstein; Edward Roy Pike; Stelios C. A. Thomopoulos; Marie-Paule Boyer; H. K. Huang; Osman M. Ratib, Editor(s)

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