Wang Bo, Yu Meifang, Zhu Xianglin, Jiang Zheyu
a School of Electrical and Information Engineering, JiangSu University , Zhenjiang , China.
b Wuxi Taihu Water Service Co., Ltd , Wuxi , China.
Prep Biochem Biotechnol. 2019;49(8):783-789. doi: 10.1080/10826068.2019.1615506. Epub 2019 May 27.
To overcome the problem that soft-sensing model cannot be updated with the bioprocess changes, this article proposed a soft-sensing modeling method which combined fuzzy c-means clustering (FCM) algorithm with least squares support vector machine theory (LS-SVM). FCM is used for separating a whole training data set into several clusters with different centers, each subset is trained by LS-SVM and sub-models are developed to fit different hierarchical property of the process. The new sample data that bring new operation information is introduced in the model, and the fuzzy membership function of the sample to each clustering is first calculated by the FCM algorithm. Then, a corresponding LS-SVM sub-model of the clustering with the largest fuzzy membership function is used for performing dynamic learning so that the model can update online. The proposed method is applied to predict the key biological parameters in the marine alkaline protease MP process. The simulation result indicates that the soft-sensing modeling method increases the model's adaptive abilities in various operation conditions and can improve its generalization ability.
为克服软测量模型无法随生物过程变化进行更新的问题，本文提出了一种将模糊C均值聚类（FCM）算法与最小二乘支持向量机理论（LS-SVM）相结合的软测量建模方法。FCM用于将整个训练数据集分离为具有不同中心的几个聚类，每个子集由LS-SVM进行训练，并开发子模型以拟合过程的不同层次特性。将带来新操作信息的新样本数据引入模型，首先通过FCM算法计算样本对每个聚类的模糊隶属度函数。然后，使用具有最大模糊隶属度函数的聚类对应的LS-SVM子模型进行动态学习，以使模型能够在线更新。将所提出的方法应用于预测海洋碱性蛋白酶MP过程中的关键生物学参数。仿真结果表明，该软测量建模方法提高了模型在各种操作条件下的自适应能力，并能提高其泛化能力。
