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). 2021 Nov 17;21(22):7635. doi: 10.3390/s21227635.
The problems that the key biomass variables in fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.
发酵过程中关键生物质变量难以实时测量的问题；本文主要提出了一种基于分段仿射（PWA）建模方法的多模型软测量建模方法，该方法采用改进的压缩因子（ICF）的粒子群优化（PSO）进行优化。首先，采用伪最近邻方法确定 PWA 模型的阶数。其次，提出了 ICF-PSO 算法，协同优化 PWA 模型的数量和每个局部模型的参数。最后，采用最小二乘支持向量机确定每个局部模型的作用域。仿真结果表明，所提出的 ICF-PSO-PWA 多模型软测量建模方法准确逼近了发酵的非线性特征，与 PSO 优化的加权最小二乘向量回归模型相比，模型预测精度提高了 4.4884%。
