A radial basis function artificial neural network (RBF ANN) based method for uncertain distributed force reconstruction considering signal noises and material dispersion-Reference-Cited by-同舟云学术

A radial basis function artificial neural network (RBF ANN) based method for uncertain distributed force reconstruction considering signal noises and material dispersion

Published:2020-06 Issue: Volume:364 Page:112954
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Wang Lei,Liu Yaru,Gu Kaixuan,Wu Tong

Funder

Aeronautical Science Foundation of China

Defense Industrial Technology Development Program

Science and Technology Foundation of China Aerospace Science

Beijing Municipal Science and Technology Commission

Beijing Advanced Discipline Center for Unmanned Aircraft System

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference37 articles.

1. Load identification using a modified modal filter technique;Mendrok;J. Vib. Control,2009

2. Time-varying wind load identification from structural responses;Law;Eng. Struct.,2005

3. Inversion of loading time history using displacement response of composite laminates: three-dimensional cases;Liu;Acta Mech.,2002

4. Structural parameters and dynamic loading identification from incomplete measurements: Approach and validation;Xu;Mech. Syst. Signal Process.,2012

5. A numerical framework for load identification and regularization with application to rolling disc problem;Ronasi;Comput. Struct.,2011

Cited by 55 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Equivalent reconstruction of local distributed dynamic load based on block sparse Bayesian learning;Measurement;2025-02

2. Dynamic force identification considering modeling errors using modal expansion method and relevant vector regression algorithm;Aerospace Science and Technology;2024-11

3. Dimensional decomposition-aided metamodels for uncertainty quantification and optimization in engineering: A review;Computer Methods in Applied Mechanics and Engineering;2024-08

4. Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification;Structural Health Monitoring;2024-07-27

5. Identification of full-field wind loads on buildings using a mechanism-inspired recursive convolutional neural network with partial structural responses;Physics of Fluids;2024-05-01