Enhanced support vector machine-based moving regression strategy for response prediction and reliability estimation of complex structure

Abstract

For predicting response property and estimating reliability level of complex structure, enhanced support vector machine-based moving regression (MR-ESVM) strategy is proposed based on support vector machine (SVM), heuristic algorithm and moving least square (MLS) technique. Under this strategy, we develop four different SVM models including SVM-based moving regression (MR-SVM), SVM-based improved moving regression (IMR-SVM), improved SVM-based moving regression (MR-ISVM) and bi-optimized SVM-based moving regression (BiOMR-SVM) methods. In these developed MR-ESVM approaches, the MR-SVM method is explored by introducing the MLS technique into the SVM model; the IMR-SVM method is discussed by fusing the MR-SVM method and artificial rabbits optimization (ARO), and the ARO is used to search the optimal radius of compact region; the MR-ISVM method is raised by integrating the ARO into the MR-SVM, and the ARO is applied to find the optimal values in the SVM model; The BiOMR-SVM method is emerged by merging the IMR-SVM and MR-ISVM methods. To verify the effectiveness of these developed MR-ESVM strategies, a multivariate nonlinear function approximation is implemented to illustrate the advantages from the mathematics perspective, an aeroengine turbine blisk radial deformation reliability analysis and an aircraft hydraulic system low pressure reliability analysis are derived to demonstrate the applicability in engineering practice. The analytical results show that these four MR-ESVM approaches hold excellent merits in modeling features and simulation characteristics. The efforts of this work provide a novel idea for the response prediction of complex structure, and enrich the reliability estimation principle of surrogate models of complex structure.