Liu Tong, Chen Sheng, Liang Shan, Gan Shaojun, Harris Chris J
IEEE Trans Neural Netw Learn Syst. 2022 May;33(5):1867-1880. doi: 10.1109/TNNLS.2020.3027701. Epub 2022 May 2.
A key characteristic of biological systems is the ability to update the memory by learning new knowledge and removing out-of-date knowledge so that intelligent decision can be made based on the relevant knowledge acquired in the memory. Inspired by this fundamental biological principle, this article proposes a multi-output selective ensemble regression (SER) for online identification of multi-output nonlinear time-varying industrial processes. Specifically, an adaptive local learning approach is developed to automatically identify and encode a newly emerging process state by fitting a local multi-output linear model based on the multi-output hypothesis testing. This growth strategy ensures a highly diverse and independent local model set. The online modeling is constructed as a multi-output SER predictor by optimizing the combining weights of the selected local multi-output models based on a probability metric. An effective pruning strategy is also developed to remove the unwanted out-of-date local multi-output linear models in order to achieve low online computational complexity without scarifying the prediction accuracy. A simulated two-output process and two real-world identification problems are used to demonstrate the effectiveness of the proposed multi-output SER over a range of benchmark schemes for real-time identification of multi-output nonlinear and nonstationary processes, in terms of both online identification accuracy and computational complexity.
生物系统的一个关键特征是能够通过学习新知识和去除过时知识来更新记忆，以便基于记忆中获取的相关知识做出明智的决策。受这一基本生物学原理的启发，本文提出了一种多输出选择性集成回归（SER）方法，用于在线识别多输出非线性时变工业过程。具体而言，开发了一种自适应局部学习方法，通过基于多输出假设检验拟合局部多输出线性模型，自动识别和编码新出现的过程状态。这种增长策略确保了高度多样化和独立的局部模型集。通过基于概率度量优化所选局部多输出模型的组合权重，将在线建模构建为多输出SER预测器。还开发了一种有效的剪枝策略，以去除不需要的过时局部多输出线性模型，从而在不牺牲预测精度的情况下实现较低的在线计算复杂度。使用一个模拟的双输出过程和两个实际识别问题，从在线识别精度和计算复杂度两方面，证明了所提出的多输出SER相对于一系列用于多输出非线性和非平稳过程实时识别的基准方案的有效性。
