The Industrial Internet of Things (IIoT) is of strategic importance in the new era of industrial big data, creating a brand-new industrial ecosystem. Considering the unknown parameters in the IIoT-based industrial process control systems, this paper combines the artificial fish swarm algorithm (AFSA) and the particle filtering (PF) algorithm into the AFSA-PF algorithm based on the self-organizing state space (SOSS) model. The AFSA-PF algorithm not only can estimates the system state but also can make the sampling distribution of the unknown parameter to move the true parameter distribution. Ultimately, the true values of the unknown parameters are identified. In this way, the system model can gradually approximate the actual IIoT-based industrial process control system.

中文翻译：

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人工鱼群算法和粒子滤波算法在工业过程控制中的应用粒子滤波算法在工业过程控制中

工业物联网（IIoT）在工业大数据的新时代具有重要的战略意义，它创建了一个全新的工业生态系统。考虑到基于IIoT的工业过程控制系统中未知参数，将人工鱼群算法（AFSA）和粒子滤波（PF）算法结合到基于自组织状态空间（SOSS）的AFSA-PF算法中模型。AFSA-PF算法不仅可以估计系统状态，还可以使未知参数的采样分布移动到真实参数分布。最终，确定未知参数的真实值。这样，系统模型可以逐渐逼近基于IIoT的实际工业过程控制系统。