A universally applicable hybrid modelling method is proposed for nonlinear industrial processes that combine the a priori process knowledge with a data‐driven model. This method constructs a unified framework for the modelling process by integrating a data‐driven modelling technique, sampling detection technique, constraint optimization problem, and an evolutionary algorithm. In the modelling process, a swarm intelligence algorithm is used to optimize the model parameters under the circumstances of satisfying the constraints of a priori knowledge. By adding the constraints of process a priori knowledge, more information can be obtained about the actual process and the over‐fitting problem can be avoided to some extent, especially when modelling a system with a small quantity of samples. In order to show the effectiveness of the method proposed in this paper, two general data‐driven models, the polynomial regression model and radial basis function network model, are used as case studies. Moreover, a function simulation experiment is designed to test effectiveness, and applied to estimate average particle size of ZrO2‐TiO2 composite colloidal sols.

中文翻译：

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结合先验知识的数据驱动模型建模方法及其在复合胶体溶胶平均粒径估计中的应用

针对非线性工业过程提出了一种通用的混合建模方法，该方法将先验过程知识与数据驱动模型相结合。该方法通过集成数据驱动的建模技术，采样检测技术，约束优化问题和进化算法，为建模过程构建了一个统一的框架。在建模过程中，采用群体智能算法在满足先验知识约束的情况下优化模型参数。通过增加过程的先验知识的约束，可以获得有关实际过程的更多信息，并且可以在某种程度上避免过拟合问题，尤其是在使用少量样本对系统进行建模时。为了证明本文提出的方法的有效性，将两个通用的数据驱动模型，多项式回归模型和径向基函数网络模型用作案例研究。此外，设计了功能模拟实验以测试有效性，并将其应用于估算ZrO2-TiO2复合胶体溶胶的平均粒径。