Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 7, Issue 2


Published
on

June 6, 2023


Pages

347-356


DOI

Article

Radial Basis Function Neural Network in Vibration Control of Civil Engineering Structure

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Authors

Jing Lu Affiliation:
School of Information and Architectural Engineering, Anhui Open University, Hefei, 230022, China
, Qinyuan Chen Affiliation:
School of Information and Architectural Engineering, Anhui Open University, Hefei, 230022, China
and Hamdy Mohamed Affiliation:
College of Engineering, Applied Science University, Bahrain


Abstract

This article uses the radial basis function artificial neural network and the MATLAB toolbox to study the vibration control of civil engineering structures. The article proposes a dynamic structure design method based on a generalized radial basis function neural network. Furthermore, the RBF neural network theory is used to optimize the structure-related controller parameters in geotechnical engineering. The research results show that RBF neural network can more accurately predict the vibration response of civil engineering. It can effectively solve the time lag problem in vibration control.


Keywords

Radial basis function, Neural network, Lyapunov stability theory, Civil engineering, Vibration control, Differential evolution algorithm, 92B20


Citation

Lu, J., Chen, Q., & Mohamed, H. (2022). Radial basis function neural network in vibration control of civil engineering structure. Applied Mathematics and Nonlinear Sciences, 7(2), 347–356. https://doi.org/10.2478/amns.2022.2.00016

Published by: Engineering Journals

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