Extraction of Piecewise-Linear Analog Circuit Models from Trained Neural Networks Using Hidden Neuron Clustering

Authors:
Simona Doboli;Gaurav Gothoskar;Alex Doboli
Affiliations:
Hofstra University;State University of New York at Stony Brook;State University of New York at Stony Brook
Venue:
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Year:
2003

Citing 2
Cited 0

Piecewise Linear Modeling and Analysis

Piecewise Linear Modeling and Analysis
Extraction of rules from artificial neural networks for nonlinear regression

IEEE Transactions on Neural Networks

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Abstract

This paper presents a new technique for automatically creating analog circuit models. The method extracts - from trained neural networks - piecewise linear models expressing the linear dependencies between circuit performances and design parameters. The paper illustrates the technique for an OTA circuit for which models for gain and bandwidth were automatically generated. The extracted models have a simple form that accurately fits the sampled points and the behavior of the trained neural networks. These models are useful for fast simulation of systems with non-linear behavior and performances.