Identification of switching linear systems using self-organizing models with application to silicon prediction in hot metal
Identification of switching linear systems using self-organizing models with application to silicon prediction in hot metal
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DOI：
10.1016/j.asoc.2016.05.048
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发表时间：
2016-10-01
影响因子：
8.7
通讯作者：
Toivonen, Hannu T.
中科院分区：
文献类型：
--
作者：
Saxen, John-Eric;Saxen, Henrik;Toivonen, Hannu T.
提出了一种基于自组织模型的分段线性切换系统的辨识方法。所提出的方法是基于制定的识别问题作为一个广义的平面聚类问题，这是解决使用自组织映射。该方法不假设系统模式取决于状态，但模式切换可能以任意且未知的方式发生。该过程不需要知道模式数或系统阶数，可用于在线和离线识别。数值算例表明，该方法能正确识别切换系统。识别方法也适用于从一个工业高炉的数据，用于建模和预测铁水的硅含量。切换线性模型被证明捕捉不同的动态过程和结果的分析揭示了如何模式切换模型逐渐和快速的变化输出。最终证明，所得模型可以深入了解不同状态下高炉中硅含量的影响因素。(C)2016爱思唯尔B.V.保留所有权利。
An approach based on self-organizing models is presented for identification of piecewise linear switching systems, whose dynamics switch between a number of modes. The proposed method is based on a formulation of the identification problem as a generalized plane-clustering problem, which is solved using self-organizing maps. The method does not assume that the system modes depend on the state, but the mode switches may occur in an arbitrary and unknown manner. The procedure does not require knowledge of the number of modes or system orders, and it can be used for both on-line and off-line identification. Numerical examples illustrate that the procedure identifies switching systems correctly. The identification method is also applied to data from an industrial blast furnace for modeling and prediction of the silicon content of the hot metal. A switched linear model is demonstrated to capture different dynamics of the process and an analysis of the results reveals how mode switching models gradual and rapid changes in the output. The resulting models are finally shown to provide insight into factors that govern the silicon content in the blast furnace in different states. (C) 2016 Elsevier B.V. All rights reserved.