NDLI: Neural networks with adaptive spline activation function

Content Provider	IEEE Xplore Digital Library
Author	Campolucci, P. Capperelli, F. Guarnieri, S. Piazza, F. Uncini, A.
Copyright Year	1996
Description	Author affiliation: Dipartimento di Elettronica e Autom., Ancona Univ., Italy (Campolucci, P.)
Abstract	In this paper a new neural network architecture, based on an adaptive activation function, called generalized sigmoidal neural network (GSNN), is proposed. The activation functions are usually sigmoidal but other functions, also depending on some free parameters, have been studied and applied. Most approaches tend to use relatively simple functions (as adaptive sigmoids), primarily due to computational complexity and difficulties hardware realization. The proposed adaptive activation function, built as a piecewise approximation with suitable cubic splines, can have arbitrary shape and allows to reduce the overall size of the neural networks, trading connection complexity with activation function complexity.
Starting Page	1442
Ending Page	1445
File Size	430982
Page Count	4
File Format	PDF
ISBN	0780331095
DOI	10.1109/MELCON.1996.551220
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-05-16
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Adaptive systems Spline Polynomials Neurons Shape Computer architecture Electronic mail Computational complexity Hardware
Content Type	Text
Resource Type	Article

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Neural networks with adaptive spline activation function