基于支持向量机算法的海水藻类生长状态软测量

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.

Soft Sensing for the State of Algae Growth in Seawater Based on Support Vector Machine Algorithm

To effectively monitor the state of algae growth in seawater,a method of soft sensing for the key representative factor based on support vector machine( SVM) algorithm was investigated. First,gridding optimization was adopted to optimize the penalty factor C and parameter σ of SVM. Then,the optimal matching parameters were used to obtain the soft sensing model of concentration of seawater chlorophyll-a via the samples training. The result of soft sensing based on SVM was compared with the result of using BP neural network. The testing result shows that the soft sensing method based on SVM has more prediction accuracy and stability than the method of BP neural network. The SVM model can be used in soft sensing for the state of seawater algae growth.