Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Hybrid C- and L-Moment–Based Hermite Transformation Models for Non-Gaussian Processes
The moment-based Hermite transformation models are widely used in extreme-value prediction and fatigue estimation of non-Gaussian processes. However, when only higher-order ordinary central moments (C-moments) are involved in the transformation, the Hermite model would lead to statistical uncertainty. Furthermore, the application of moment-based Hermite models to measured time series is restricted if accurate moments cannot be retrieved from data. In this paper, the respective virtues of C-moments and linear moments (L-moments) are exploited to formulate a new style of nonlinear transformation. Combinations of these two types of moments are sought with various strategies in terms of the accuracy in extreme-value prediction of non-Gaussian processes. It is found that for a process of very strong non-Gaussianity, the quartic C-moment model renders best accuracy when the sampling data are rich, while two of hybrid C- and L-moment (C/L) models work most nicely when data size is limited.
Hybrid C- and L-Moment–Based Hermite Transformation Models for Non-Gaussian Processes
The moment-based Hermite transformation models are widely used in extreme-value prediction and fatigue estimation of non-Gaussian processes. However, when only higher-order ordinary central moments (C-moments) are involved in the transformation, the Hermite model would lead to statistical uncertainty. Furthermore, the application of moment-based Hermite models to measured time series is restricted if accurate moments cannot be retrieved from data. In this paper, the respective virtues of C-moments and linear moments (L-moments) are exploited to formulate a new style of nonlinear transformation. Combinations of these two types of moments are sought with various strategies in terms of the accuracy in extreme-value prediction of non-Gaussian processes. It is found that for a process of very strong non-Gaussianity, the quartic C-moment model renders best accuracy when the sampling data are rich, while two of hybrid C- and L-moment (C/L) models work most nicely when data size is limited.
Hybrid C- and L-Moment–Based Hermite Transformation Models for Non-Gaussian Processes
Gao, S. (Autor:in) / Zheng, X. Y. (Autor:in) / Huang, Y. (Autor:in)
12.12.2017
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Estimation of Peak Factor of Non-Gaussian Wind Pressures by Improved Moment-Based Hermite Model
| Online Contents | 2017
Estimation of Peak Factor of Non-Gaussian Wind Pressures by Improved Moment-Based Hermite Model
| Online Contents | 2017