Dynamical pattern recognition for univariate time series and its application to an axial compressor
作者机构：Center for Intelligent Medical EngineeringSchool of Control Science and TechnologyShandong UniversityJinan250061ShandongChina School of Automation Science and EngineeringSouth China University of TechnologyGuangzhou510641GuangdongChina
出 版 物：《Control Theory and Technology》 (控制理论与技术（英文版）)
年 卷 期：2024年第22卷第1期
页 面：39-55页
核心收录：
学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 0811[工学-控制科学与工程]
基 金：supported by the National Postdoctoral Researcher Program of China(No.GZC20231451) the National Natural Science Foundation of China(Nos.61890922,62203263) the Shandong Province Natural Science Foundation(Nos.ZR2020ZD40,ZR2022QF062).
主 题：Dynamical pattern recognition Deterministic learning Stall warning Radial basis function network Sampled-data observer
摘 要：In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.
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