Identification of systems with slowly sampled output is studied. A linear parameter varying (LPV) model with multi-model structure is used to solve the problem. The output error (OE) method is used to estimate model parameters. Firstly, the local models and weighting functions are estimated separately using optimization methods. Then, a relaxation iteration method is developed to refine the parameters of the total model. For LPV model structure determination, an engineering approach is proposed that combines process knowledge with the so-called final output error criteria (FOE). The method is verified using both simulation data and industrial data. In the industrial case study, the LPV models give more accurate prediction of product qualities than that of a linear dynamic model and that of a static nonlinear model; the result also indicates the necessity of using test signals in soft-sensor development.

研究了具有缓慢采样输出的系统的识别。具有多模型结构的线性参数变化（LPV）模型用于解决该问题。输出误差（OE）方法用于估计模型参数。首先，使用优化方法分别估计局部模型和加权函数。然后，开发了松弛迭代方法来细化总模型的参数。对于LPV模型结构确定，提出了一种工程方法，该方法将过程知识与所谓的最终输出误差标准（FOE）相结合。使用仿真数据和工业数据验证了该方法。在工业案例研究中，与线性动态模型和静态非线性模型相比，LPV模型可以更准确地预测产品质量；