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基于支持向量机算法的海水藻类生长状态软测量

Keywords: 支持向量机算法,藻类生长状态,参数寻优,软测量,神经网络

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Abstract:

为了有效监测海水藻类生长状态,采用支持向量机算法对水体中关键表征因子进行软测量.首先采用网格寻优法对支持向量机(SVM)的惩罚因子C和参数σ进行参数寻优,然后利用所得最佳匹配参数通过样本训练,获得海水叶绿素-a浓度的软测量模型.将基于SVM的软测量结果与基于BP神经网络的软测量结果作对比,可以看出,基于SVM的软测量方法具有较好的预测精度和稳定性,可应用于海水藻类生长状态的软测量.

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