Dynamical pattern recognition for univariate time series and its application to an axial compressor
Jingtao Hu1
Weiming Wu1
Zejian Zhu2
Cong Wang1
1.Center for Intelligent Medical Engineering,School of Control Science and Technology,Shandong University,Jinan 250061,Shandong,China2.School of Automation Science and Engineering,South China University of Technology,Guangzhou 510641,Guangdong,China
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.
机标关键词：applicationseriestimeaxialcompressordynamicalpatternrecognition
论文发表日期：2024-02-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：17( 39-55 )
英文信息