Abstract
ABSTRACTFor effectively estimating the reliability of complex structures, a least squares support vector machine with variable selection and hyperparameter optimization (SVMSO, short for) is proposed based on local linear embedding with Pearson coefficient and location density with particle swarm optimization (LDPSO) algorithm. In this proposed method, the local linear embedding with Pearson coefficient is used to select the variables that have a strong correlation with output responses, which are embedded in relatively low‐dimensional space to avoid the negative influence of high‐dimensional input parameters. The optimal hyperparameters of least squares support vector machines (LSSVM) are obtained by applying the LDPSO to improve the accuracy of LSSVM affected by the hyperparameters. Taking civil aircraft turbine blisk as a study case, the effectiveness and applicability of SVMSO are verified in aspects of modeling quality and simulation characteristics, by comparing direct simulation, support vector machine, and LSSVM. The case results and conclusions represent that the proposed method has good precision and efficiency under a high‐dimensional data scale, and is suitable for reliability analysis of complex structures.
Published Version
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