By Topic

Piecewise-linear modeling of analog circuits based on model extraction from trained neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Gothoskar, G. ; Dept. of Electr. & Comput. Eng., New York State Univ. at Stony Brook, NY, USA ; Doboli, A. ; Doboli, S.

This paper presents a new technique for automatically creating analog circuit models. The method extracts piecewise linear models from trained neural networks. A model is a set of linear dependencies between circuit performance and design parameters. The paper illustrates the technique for an OTA circuit for which models for gain and bandwidth are generated. As experiments show, the obtained models have simple form that accurately fits the sampled points. These models are useful for fast simulation of systems with nonlinear behavior and performance.

Published in:

Behavioral Modeling and Simulation, 2002. BMAS 2002. Proceedings of the 2002 IEEE International Workshop on

Date of Conference:

6-8 Oct. 2002