An adaptive RBF network optimised using a genetic algorithm applied to rainfall forecasting

C. Jareanpon; W. Pensuwon; R. Frank; N. Davey

doi:10.1109/ISCIT.2004.1413871

An adaptive RBF network optimised using a genetic algorithm applied to rainfall forecasting

C. Jareanpon
, W. Pensuwon
, R. Frank
, N. Davey

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Citations (Scopus)

49 Downloads (Pure)

Abstract

Rainfall prediction is a challenging task especially in a modern world facing the major environmental problem of global warming. The proposed method uses an Adaptive Radial Basis Function neural network mode with a specially designed gerietic algoruhm (CA) to obtain the optimal model parameters. A significant feature of the Adaptive Radinl Basis Function network is that it is able creak new hidden units and solve the spread factor problem using a genetic algorithm. It is shown that the evolved parameter values improved performance.

Original language	English
Title of host publication	In: Procs IEEE Int Symp on Communications and Information Technologies (ISCIT 2004) Vol.2
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1005-1010
ISBN (Print)	0-7803-8593-4
DOIs	https://doi.org/10.1109/ISCIT.2004.1413871
Publication status	Published - 2004

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title = "An adaptive RBF network optimised using a genetic algorithm applied to rainfall forecasting",

abstract = "Rainfall prediction is a challenging task especially in a modern world facing the major environmental problem of global warming. The proposed method uses an Adaptive Radial Basis Function neural network mode with a specially designed gerietic algoruhm (CA) to obtain the optimal model parameters. A significant feature of the Adaptive Radinl Basis Function network is that it is able creak new hidden units and solve the spread factor problem using a genetic algorithm. It is shown that the evolved parameter values improved performance.",

author = "C. Jareanpon and W. Pensuwon and R. Frank and N. Davey",

note = "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.---- Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. DOI : 10.1109/ISCIT.2004.1413871",

year = "2004",

doi = "10.1109/ISCIT.2004.1413871",

language = "English",

isbn = "0-7803-8593-4",

pages = "1005--1010",

booktitle = "In: Procs IEEE Int Symp on Communications and Information Technologies (ISCIT 2004) Vol.2",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

}

An adaptive RBF network optimised using a genetic algorithm applied to rainfall forecasting. / Jareanpon, C.; Pensuwon, W.; Frank, R. et al.
In: Procs IEEE Int Symp on Communications and Information Technologies (ISCIT 2004) Vol.2. Institute of Electrical and Electronics Engineers (IEEE), 2004. p. 1005-1010.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Pensuwon, W.

AU - Frank, R.

AU - Davey, N.

N1 - This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.---- Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. DOI : 10.1109/ISCIT.2004.1413871

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AB - Rainfall prediction is a challenging task especially in a modern world facing the major environmental problem of global warming. The proposed method uses an Adaptive Radial Basis Function neural network mode with a specially designed gerietic algoruhm (CA) to obtain the optimal model parameters. A significant feature of the Adaptive Radinl Basis Function network is that it is able creak new hidden units and solve the spread factor problem using a genetic algorithm. It is shown that the evolved parameter values improved performance.

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DO - 10.1109/ISCIT.2004.1413871

M3 - Conference contribution

SN - 0-7803-8593-4

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BT - In: Procs IEEE Int Symp on Communications and Information Technologies (ISCIT 2004) Vol.2

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

An adaptive RBF network optimised using a genetic algorithm applied to rainfall forecasting

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