An approximation by neural networkswith a fixed weight

Authors:
Nahmwoo Hahm;Bum Il Hong
Affiliations:
-;-
Venue:
Computers & Mathematics with Applications
Year:
2004

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Abstract

Neural networks are widely used in many applications including astronomical physics,image processing, recognition, robotics, and automated target tracking, etc. Their ability to approximate arbitrary functions is the main reason for this popularity. In this paper, we discuss the constructive approximation on the whole real line by a neural networks with a sigmoidal activation function and a fixed weight. Using the convolution method, we show neural network approximation with a fixed weight to a continuous function on a compact interval. Also, we demonstrate a computational work that shows good agreement with theory.