Investigation and comparison of generalization ability of multi-layer perceptron and radial basis function artificial neural networks for signal power loss prediction

Virginia C. Ebhota; Joseph Isabona; Viranjay M. Srivastava

doi:10.15866/irecap.v9i1.15330

Investigation and comparison of generalization ability of multi-layer perceptron and radial basis function artificial neural networks for signal power loss prediction

Virginia C. Ebhota^*, Joseph Isabona, Viranjay M. Srivastava

^*Corresponding author for this work

University of KwaZulu-Natal

Research output: Contribution to journal › Article › peer-review

12 Citations (SciVal)

Original language	English
Pages (from-to)	46-54
Number of pages	9
Journal	International Journal on Communications Antenna and Propagation
Volume	9
Issue number	1
DOIs	https://doi.org/10.15866/irecap.v9i1.15330
Publication status	Published (VoR) - Feb 2019

Keywords

Artificial neural networks
Bayesian regularization algorithm
Propagation
Propagation prediction

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10.15866/irecap.v9i1.15330

Cite this

@article{eab4a3a1ac1e43c0a5e362f325b92f98,

title = "Investigation and comparison of generalization ability of multi-layer perceptron and radial basis function artificial neural networks for signal power loss prediction",

keywords = "Artificial neural networks, Bayesian regularization algorithm, Propagation, Propagation prediction",

author = "Ebhota, \{Virginia C.\} and Joseph Isabona and Srivastava, \{Viranjay M.\}",

note = "Publisher Copyright: {\textcopyright} 2019 Praise Worthy Prize S.r.l.",

year = "2019",

month = feb,

doi = "10.15866/irecap.v9i1.15330",

language = "English",

volume = "9",

pages = "46--54",

journal = "International Journal on Communications Antenna and Propagation",

issn = "2039-5086",

number = "1",

}

Investigation and comparison of generalization ability of multi-layer perceptron and radial basis function artificial neural networks for signal power loss prediction. / Ebhota, Virginia C.; Isabona, Joseph; Srivastava, Viranjay M.
In: International Journal on Communications Antenna and Propagation, Vol. 9, No. 1, 02.2019, p. 46-54.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Investigation and comparison of generalization ability of multi-layer perceptron and radial basis function artificial neural networks for signal power loss prediction

AU - Ebhota, Virginia C.

AU - Isabona, Joseph

AU - Srivastava, Viranjay M.

PY - 2019/2

Y1 - 2019/2

KW - Artificial neural networks

KW - Bayesian regularization algorithm

KW - Propagation

KW - Propagation prediction

UR - https://www.scopus.com/pages/publications/85066017732

UR - https://www.scopus.com/inward/citedby.url?scp=85066017732&partnerID=8YFLogxK

U2 - 10.15866/irecap.v9i1.15330

DO - 10.15866/irecap.v9i1.15330

M3 - Article

AN - SCOPUS:85066017732

SN - 2039-5086

VL - 9

SP - 46

EP - 54

JO - International Journal on Communications Antenna and Propagation

JF - International Journal on Communications Antenna and Propagation

IS - 1

ER -

Investigation and comparison of generalization ability of multi-layer perceptron and radial basis function artificial neural networks for signal power loss prediction

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