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Authors: Liu Zaiwen and Lv Siying

Affiliation: Beijing Technology and Business University, China

ISBN: 978-989-8565-39-6

ISSN: 2184-433X

Keyword(s): Predicting Method, Water Bloom, RBF Neural Network, Squares Support Vector Machine, Intelligence.

Related Ontology Subjects/Areas/Topics: Agents ; AI and Creativity ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bioinformatics ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Information Systems Analysis and Specification ; Intelligent Control Systems and Optimization ; Methodologies and Methods ; Methodologies and Technologies ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Operational Research ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Simulation ; Soft Computing ; Theory and Methods

Abstract: Water bloom is one phenomena of eutrophication, and water bloom prediction is always a challenge. A short-term intelligent predicting method based on RBF neural network (RBFNN), and medium-term intelligent predicting method based on least squares support vector machine (LSSVM) for water bloom are proposed in this paper. Including research on the monitoring learning algorithms to the center, width and weight of basis function of RBF network, the width of RBF and fitting and generalization abilities of network, and the function and influence, which the number of RBF hidden level nodes brings to the performance of network, as well as error-corrected algorithm based on gradient descent are analyzed. Least squares support machine, which has long prediction period and high degree of prediction accuracy, needs a small amount of sample can be used to predict the medium-term change discipline of Chl-a (Chlorophyll-a) well. The results of simulation and application show that: RBF neural networ k can be used to forecast the change of Chl-a in short term well, and LSSVM improves the algorithm of support vector machine (SVM), and it has long-term prediction period, strong generalization ability and high prediction accuracy; and this model provides an efficient new way for medium-term water bloom prediction. (More)

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Paper citation in several formats:
Zaiwen, L.; Qiaowei, W. and Siying, L. (2013). Intelligent Predicting Method of Water Bloom based RBFNN and LSSVM.In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, ISSN 2184-433X, pages 592-597. DOI: 10.5220/0004334705920597

@conference{icaart13,
author={Liu Zaiwen. and Wu Qiaowei. and Lv Siying.},
title={Intelligent Predicting Method of Water Bloom based RBFNN and LSSVM},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2013},
pages={592-597},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004334705920597},
isbn={978-989-8565-39-6},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Intelligent Predicting Method of Water Bloom based RBFNN and LSSVM
SN - 978-989-8565-39-6
AU - Zaiwen, L.
AU - Qiaowei, W.
AU - Siying, L.
PY - 2013
SP - 592
EP - 597
DO - 10.5220/0004334705920597

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