An Online Self-Constructive Normalized Gaussian Network with Localized Forgetting-Reference-Cited by-同舟云学术

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An Online Self-Constructive Normalized Gaussian Network with Localized Forgetting

Published:2017 Issue:3 Volume:E100.A Page:865-876
ISSN:0916-8508
Container-title:IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
language:en
Short-container-title:IEICE Trans. Fundamentals

Author:

BACKHUS Jana¹,TAKIGAWA Ichigaku¹²,IMAI Hideyuki¹,KUDO Mineichi¹,SUGIMOTO Masanori¹

Affiliation:

1. Department of Computer Science and Information Technology, Graduate School of Information Science and Technology, Hokkaido University

2. JST PRESTO

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Signal Processing

Link

https://www.jstage.jst.go.jp/article/transfun/E100.A/3/E100.A_865/_pdf

Reference19 articles.

1. [1] G. Bugmann, “Normalized Gaussian radial basis function networks,” Neurocomputing, vol.20, no.1-3, pp.97-110, Aug. 1998.

2. [2] E. Celaya and A. Agostini, “On-line EM with weight-based forgetting,” Neural Comput., vol.27, no.5, pp.1142-1157, May 2015.

3. [3] M.R. Cowper, B. Mulgrew, and C.P. Unsworth, “Nonlinear prediction of chaotic signals using a normalised radial basis function network,” Signal Process., vol.82, no.5, pp.775-789, May 2002.

4. [4] P. Grassberger and I. Procaccia, “Characterization of strange attractors,” Phys. Rev. Lett., vol.50, no.5, pp.346-349, Jan. 1983.

5. [5] C. Hennig, “Methods for merging Gaussian mixture components,” Advances in Data Analysis and Classification, vol.4, no.1, pp.3-34, 2010.

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