Adaptive deformation feature analysis based on Gaussian process regression and its application
Due to the intricate nature of deformation-causing factors,classic regression models struggle to accommodate the nonlinearity and dynamism presented by deformations.This paper extends the theory of standard Gaussian Process Regression(GPR)to address the challenges faced by GPR in deformation analysis,specifically in online self-learning and adaptive feature presentation.Firstly,by optimizing the training sample set and synchronously updating samples and hyperparameters online,the online learning capability of GPR is enhanced,thereby improving its computational performance in handling real-time data streams.Secondly,by analyzing the nonlinear characteristics of residual sequences and applying Fourier transform techniques to extract periodic features,an online adaptive generation of feature kernel functions matching the deformation features is achieved.The results of simulation experiment and engineering applications indicated that the adaptively generated feature kernel functions outperform standard kernel functions in extracting nonlinear deformation features while retaining periodic features.The research findings can be applied to the online analysis of deformation features in automated deformation monitoring systems,ensuring the near real-time analysis of monitoring data and the reliability of analysis results.
deformation monitoringgaussian process regressionkernel functionfeature analysis
万方数据
维普
