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Proceedings Paper

A Neural Network For L1 Norm Linear Regression
Author(s): James P. Helferty; James A. Stover; John J. Helferty
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Paper Abstract

We are presenting a neural network optimization circuit for robust regression. The minimization of Least Square Error (LSE), L2 norm, cost functions is predominantly used for fitting functions to data points, but LSE approaches are highly sensitive to outlier points, or points that don't follow the trend. To alleviate this problem, we replace the L2 norm error function by an L1 norm error function which sums the absolute deviation of the errors and puts less weight on outlier points. An analog neural network optimization circuit is then developed to minimize the sum of the L1 norm error function. Simulation examples are presented on example data sets that compare the neural network solution with LSE solution .

Paper Details

Date Published: 1 February 1990
PDF: 12 pages
Proc. SPIE 1196, Intelligent Control and Adaptive Systems, (1 February 1990); doi: 10.1117/12.969918
Show Author Affiliations
James P. Helferty, The Pennsylvania State University (United States)
James A. Stover, The Pennsylvania State University (United States)
John J. Helferty, Temple University (United States)

Published in SPIE Proceedings Vol. 1196:
Intelligent Control and Adaptive Systems
Guillermo Rodriguez, Editor(s)

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