| SOM网络与SVM在水质富营养化评价中的对比 |
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| 引用本文： | 石欣,熊庆宇,雷璐宁.SOM网络与SVM在水质富营养化评价中的对比[J].重庆大学学报(自然科学版),2010,33(3):119-123. |
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| 作者姓名： | 石欣 熊庆宇 雷璐宁 |
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| 作者单位： | 重庆大学,自动化学院,重庆,400044;重庆大学,自动化学院,重庆,400044;重庆大学,自动化学院,重庆,400044 |
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| 基金项目： | 国家科技重大专项(2009ZX07528-003-09);;重庆市科技重大攻关项目(CSTC,2006AA7024) |
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| 摘 要： | 针对复杂水环境中的富营养化评价问题,利用三峡库区水体富营养化监测数据,对自组织映射神经网络和支持向量机模型在解决该评价问题上的性能表现进行对比研究。实验结果表明,2种模型均有较快的计算速度和较高的精度,但与自组织映射网络模型相比,支持向量机模型具有更好的稳定性和抗干扰能力,在参数选择上更为简单。
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| 关 键 词： | 支持向量机 自组织映射 神经网络 对比 评价 富营养化 |
| 收稿时间： | 2009/11/10 0:00:00 |
A comparative study of eutrophication evaluation models based on SOM neural network and SVM |
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| SHI Xin,XIONG Qing yu and LEI Lu ning.A comparative study of eutrophication evaluation models based on SOM neural network and SVM[J].Journal of Chongqing University(Natural Science Edition),2010,33(3):119-123. |
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| Authors: | SHI Xin XIONG Qing yu and LEI Lu ning |
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| Institution: | Automation Department;Chongqing University;Chongqing 400044;P.R.China |
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| Abstract: | Considering the eutrophication evaluation problems of complicated water environment,using the eutrophication inspection data of the Three Gorges Reservoir district to compare of the eutrophication evaluation models' performances based SOM neural network and SVM.The results shows that both models have fast calculation speed and high precision,but compared to SOM neural network model,the SVM model has better stability and anti-jamming capability,also its preferences are simpler. |
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| Keywords: | support vector machine self organizing maps neural networks comparison evaluation eutrophication |
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