| Dynamical pattern recognition for univariate time series and its application to an axial compressor |
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| 引用本文： | Jingtao Hu,Weiming Wu,Zejian Zhu,Cong Wang. Dynamical pattern recognition for univariate time series and its application to an axial compressor[J]. 控制理论与应用(英文版), 2024, 22(1): 39-55 |
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| 作者姓名： | Jingtao Hu Weiming Wu Zejian Zhu Cong Wang |
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| 作者单位： | 1 Center for Intelligent Medical Engineering, School of Control Science and Technology, Shandong University, Jinan 250061, Shandong, China;;2 School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, Guangdong, China. |
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| 基金项目： | This work was supported by the National Postdoctoral Researcher Program of China (No. GZC20231451), the National Natural Science Foundation of China (Nos. 61890922, 62203263) and the Shandong Province Natural Science Foundation (Nos. ZR2020ZD40, ZR2022QF062). |
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| 摘 要： | In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurementsof general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe theunivariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is thenadopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and themodeling 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 testunivariate 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, simulationstudies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.
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| 关 键 词： | Dynamical pattern recognition · Deterministic learning · Stall warning · Radial basis function network · Sampled-data observer |
Dynamical pattern recognition for univariate time series and its application to an axial compressor |
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| Jingtao Hu,Weiming Wu,Zejian Zhu,Cong Wang. Dynamical pattern recognition for univariate time series and its application to an axial compressor[J]. Journal of Control Theory and Applications, 2024, 22(1): 39-55 |
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| Authors: | Jingtao Hu Weiming Wu Zejian Zhu Cong Wang |
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| Affiliation: | 1 Center for Intelligent Medical Engineering, School of Control Science and Technology, Shandong University, Jinan 250061, Shandong, China;;2 School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, Guangdong, China. |
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| Abstract: | In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurementsof general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe theunivariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is thenadopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and themodeling 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 testunivariate 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, simulationstudies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach. |
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| Keywords: | Dynamical pattern recognition · Deterministic learning · Stall warning · Radial basis function network · Sampled-data observer |
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