Lu Na, Wang Bo, Zhu Xianglin
Key Laboratory of Agricultural Measurement and Control Technology and Equipment for Mechanical Industrial Facilities, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel). 2023 Nov 11;23(22):9119. doi: 10.3390/s23229119.
Due to the highly nonlinear, multi-stage, and time-varying characteristics of the marine lysozyme fermentation process, the global soft sensor models established using traditional single modeling methods cannot describe the dynamic characteristics of the entire fermentation process. Therefore, this study proposes a weighted ensemble learning soft sensor modeling method based on an improved seagull optimization algorithm (ISOA) and Gaussian process regression (GPR). First, an improved density peak clustering algorithm (ADPC) was used to divide the sample dataset into multiple local sample subsets. Second, an improved seagull optimization algorithm was used to optimize and transform the Gaussian process regression model, and a sub-prediction model was established. Finally, the fusion strategy was determined according to the connectivity between the test samples and local sample subsets. The proposed soft sensor model was applied to the prediction of key biochemical parameters of the marine lysozyme fermentation process. The simulation results show that the proposed soft sensor model can effectively predict the key biochemical parameters with relatively small prediction errors in the case of limited training data. According to the results, this model can be expanded to the soft sensor prediction applications in general nonlinear systems.
由于海洋溶菌酶发酵过程具有高度非线性、多阶段和时变特性，使用传统单一建模方法建立的全局软传感器模型无法描述整个发酵过程的动态特性。因此，本研究提出了一种基于改进海鸥优化算法（ISOA）和高斯过程回归（GPR）的加权集成学习软传感器建模方法。首先，使用改进的密度峰值聚类算法（ADPC）将样本数据集划分为多个局部样本子集。其次，使用改进的海鸥优化算法对高斯过程回归模型进行优化和变换，建立子预测模型。最后，根据测试样本与局部样本子集之间的连通性确定融合策略。将所提出的软传感器模型应用于海洋溶菌酶发酵过程关键生化参数的预测。仿真结果表明，所提出的软传感器模型在训练数据有限的情况下，能够以相对较小的预测误差有效预测关键生化参数。根据结果，该模型可扩展到一般非线性系统的软传感器预测应用中。
