| 基于LS-SVM的养殖水体氨氮含量分析模型的优化 |
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| 引用本文： | 居锦武.基于LS-SVM的养殖水体氨氮含量分析模型的优化[J].大连海洋大学学报,2016(4):444-448. |
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| 作者姓名： | 居锦武 |
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| 作者单位： | 四川理工学院 计算机学院,四川 自贡,643000 |
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| 基金项目： | 四川省科技支撑计划项目(15ZC0195)，四川省院士(专家)工作站基金资助项目(2014YSGZZ01)，企业信息化与物联网测控技术四川省高校重点实验室基金资助项目(2014WYY01)，泸州老窖奖学金资助项目(15LJZK04) |
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| 摘 要： | 水体氨氮浓度是水产养殖的关键水质指标,为了提高氨氮浓度的测量精度,减少测量过程中p H、温度、静置时间等因素对准确度的影响,使用最小二乘支持向量机(LS-SVM)算法建立了分析预测模型,通过正交试验仿真测试,获取各因素的最佳优化组合为p H值10.5、反应温度35℃、静置时间20 min、检测光源波长380 nm。仿真结果表明,在设计在线式氨氮检测系统时,利用最佳优化组合对氨氮浓度分析模型进行优化,提高了氨氮浓度的测量精度。
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| 关 键 词： | 支持向量机 水产养殖 氨氮 分析模型 |
Optimization of an ammonia nitrogen content analysis model in aquaculture water based on LS-SVM |
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| Abstract: | The least square support vector machine ( LS-SVM) was used to establish an analytical prediction model of ammonia nitrogen concentration as a key water quality index in aquaculture in order to improve the measurement accuracy of ammonia nitrogen concentration, and the analytical prediction model was optimized to reduce the influ-ence of pH value, temperature, static time and otherfactors during measurement process by an orthogonal simulation test. The results showed that the precise measurement of ammonia nitrogen concentration was observed under the conditions of pH value of 10. 5, temperature of 35 ℃, static time of 20 min, and light source wave length of 380 nm using an on-line ammonia nitrogen detection system. |
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| Keywords: | support vector machine aquaculture ammonia nitrogen analysis model |
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