Evolving neural networks for chlorophyll-a prediction

Xin YAO; Yong LIU

doi:10.1109/ICCIMA.2001.970465

Evolving neural networks for chlorophyll-a prediction

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

4 Citations (Scopus)

Abstract

The paper studies the application of evolutionary artificial neural networks to chlorophyll-a prediction in Lake Kasumigaura (in Japan). Unlike previous applications of artificial neural networks in this field, the architecture of the artificial neural network is evolved automatically rather than designed manually. The evolutionary system is able to find a near optimal architecture of the artificial neural network for the prediction task. Our experimental results have shown that evolved artificial neural networks are very compact and generalise well. The evolutionary system is able to explore a large space of possible artificial neural networks and discover novel artificial neural networks for solving a problem. © 2001 IEEE.

Original language	English
Title of host publication	Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	185-189
Number of pages	5
ISBN (Print)	0769513123
DOIs	https://doi.org/10.1109/ICCIMA.2001.970465
Publication status	Published - 13 Nov 2002
Externally published	Yes

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10.1109/ICCIMA.2001.970465

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title = "Evolving neural networks for chlorophyll-a prediction",

abstract = "The paper studies the application of evolutionary artificial neural networks to chlorophyll-a prediction in Lake Kasumigaura (in Japan). Unlike previous applications of artificial neural networks in this field, the architecture of the artificial neural network is evolved automatically rather than designed manually. The evolutionary system is able to find a near optimal architecture of the artificial neural network for the prediction task. Our experimental results have shown that evolved artificial neural networks are very compact and generalise well. The evolutionary system is able to explore a large space of possible artificial neural networks and discover novel artificial neural networks for solving a problem. {\textcopyright} 2001 IEEE.",

author = "Xin YAO and Yong LIU",

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doi = "10.1109/ICCIMA.2001.970465",

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Evolving neural networks for chlorophyll-a prediction. / YAO, Xin; LIU, Yong.
Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001. Institute of Electrical and Electronics Engineers Inc., 2002. p. 185-189 970465.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AB - The paper studies the application of evolutionary artificial neural networks to chlorophyll-a prediction in Lake Kasumigaura (in Japan). Unlike previous applications of artificial neural networks in this field, the architecture of the artificial neural network is evolved automatically rather than designed manually. The evolutionary system is able to find a near optimal architecture of the artificial neural network for the prediction task. Our experimental results have shown that evolved artificial neural networks are very compact and generalise well. The evolutionary system is able to explore a large space of possible artificial neural networks and discover novel artificial neural networks for solving a problem. © 2001 IEEE.

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DO - 10.1109/ICCIMA.2001.970465

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BT - Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001

PB - Institute of Electrical and Electronics Engineers Inc.

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Evolving neural networks for chlorophyll-a prediction

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