时变过程在线辨识的即时递推核学习方法研究

为了及时跟踪非线性化工过程的时变特性, 提出即时递推核学习 (Kernel learning, KL)的在线辨识方法. 针对待预测的新样本点, 采用即时学习 (Just-in-time kernel learning, JITL)策略, 通过构造累积相似度因子, 选择与其相似的样本集建立核学习辨识模型. 为避免传统即时学习对每个待预测点都重新建模的繁琐, 利用两个临近时刻相似样本集的异同点, 采用递推方法有效添加新样本, 并删减旧模型的样本, 以快速建立新即时模型. 通过一时变连续搅拌釜式反应过程的在线辨识, 表明了所提出方法在保证计算效率的同时, 较传统递推核学习方法提高了辨识的准确程度, 能更好地辨识时变过程.

Online Identification of Time-varying Processes Using Just-in-time Recursive Kernel Learning Approach

An online identification method using just-in-time recursive kernel learning (KL) is proposed to trace the time-varying characteristics of nonlinear chemical processes. For each query sample, a just-in-time kernel learning (JITL) model is established using the similar set constructed by a presented cumulative similarity factor. Different from traditional just-in-time learning approaches discarding their models at each time, an efficient modeling strategy is proposed to reduce the computational load by utilizing the similarity between two neighborhood models. Consequently, a new just-in-time kernel learning model can be quickly constructed using the recursive updating algorithm, by introducing new samples and deleting different samples. The superiority of the proposed online identification method is demonstrated by a continuous stirred tank reactor process with time-varying parameters, showing better prediction performance compared with conventional recursive kernel learning approaches.