Layered Neural Networks with Gaussian Hidden Units as Universal Approximations-Reference-Cited by-同舟云学术

Layered Neural Networks with Gaussian Hidden Units as Universal Approximations

Published:1990-06 Issue:2 Volume:2 Page:210-215
ISSN:0899-7667
Container-title:Neural Computation
language:en
Short-container-title:Neural Computation

Author:

Hartman Eric J.¹,Keeler James D.¹,Kowalski Jacek M.²

Affiliation:

1. Microelectronics and Computer Technology Corp. (MCC), 3500 West Balcones Center Drive, Austin, TX 78759-6509 USA

2. Department of Physics, University of North Texas, P.O. Box 5368, Denton, TX 76203-5368 USA

Abstract

A neural network with a single layer of hidden units of gaussian type is proved to be a universal approximator for real-valued maps defined on convex, compact sets of Rn.

Publisher

MIT Press - Journals

Subject

Cognitive Neuroscience,Arts and Humanities (miscellaneous)

Link

https://www.mitpressjournals.org/doi/pdf/10.1162/neco.1990.2.2.210

Reference6 articles.

1. Product Units: A Computationally Powerful and Biologically Plausible Extension to Backpropagation Networks

2. Ergodic theory of chaos and strange attractors

3. On the approximate realization of continuous mappings by neural networks

4. Fast Learning in Networks of Locally-Tuned Processing Units

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