Huang Feixiang, Li Longhao, Du Chuanxiang, Wang Shuang, Liu Xuefeng
School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, 255000, China.
Sci Rep. 2024 Sep 2;14(1):20349. doi: 10.1038/s41598-024-71161-4.
In the process of penicillin fermentation, there is a strong nonlinear relationship between the input eigenvector and multiple output vectors, which makes the prediction accuracy of the existing model difficult to meet the requirements of chemical production. Therefore, a local selective ensemble learning multi-objective soft sensing modeling strategy is proposed in this study. Firstly, a localization method based on transfer entropy and k-means is proposed to reconstruct the sample set. Then, based on the reconstructed local samples, the local soft sensing model is established by the multi-objective support vector regression method, and the selective ensemble of sub-models and the adaptive calculation of prediction weights are realized. At the same time, to reduce the adverse effects caused by improper selection of model parameters, the sparrow search algorithm is used to realize the tuning of the mentioned model parameters. Finally, the proposed modeling strategy is simulated. The results show that, compared with other methods, the proposed local selective ensemble learning multi-objective soft sensing modeling strategy has better prediction performance.
在青霉素发酵过程中，输入特征向量与多个输出向量之间存在很强的非线性关系，这使得现有模型的预测精度难以满足化工生产的要求。因此，本研究提出了一种局部选择性集成学习多目标软测量建模策略。首先，提出了一种基于转移熵和k均值的定位方法来重构样本集。然后，基于重构后的局部样本，采用多目标支持向量回归方法建立局部软测量模型，实现子模型的选择性集成和预测权重的自适应计算。同时，为减少模型参数选择不当带来的不利影响，采用麻雀搜索算法实现上述模型参数的整定。最后，对所提出的建模策略进行了仿真。结果表明，与其他方法相比，所提出的局部选择性集成学习多目标软测量建模策略具有更好的预测性能。
