Modeling and prediction of violent abnormal vibration of large rolling mills based on chaos and wavelet neural networks

Authors:
Zhonghui Luo;Xiaozhen Wang;Xiaoning Xue;Baihai Wu;Yibin Yu
Affiliations:
College of Engineering, Zhanjiang Ocean University, Zhanjiang, Guangdong, China;College of Engineering, Zhanjiang Ocean University, Zhanjiang, Guangdong, China;College of Engineering, Zhanjiang Ocean University, Zhanjiang, Guangdong, China;Mechanical and Electronic Engineering College, Guangdong University of Technology, Guangzhou, Guangdong, China;College of Engineering, Zhanjiang Ocean University, Zhanjiang, Guangdong, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

Citing 3
Cited 0

Practical method for determining the minimum embedding dimension of a scalar time series

Physica D
Wavelet neural networks for function learning

IEEE Transactions on Signal Processing
Runge-Kutta neural network for identification of dynamical systems in high accuracy

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper analyses the chaotic characteristics of violent abnormal vibration signals of a large rolling mill, and studies phase space reconstruction techniques of the signals. On this basis, the vibration model of wavelet neural networks and the model of backpropagation neural networks are set up, respectively, through inversion methods. The properties of these two models are tested and compared with each other. The result shows that the wavelet neural networks have an advantage over the backpropagation neural networks in rapid convergence and high accuracy.