Application of Grey Neural Network in Vehicle Vibration Prediction

2018 ◽  
Vol 07 (01) ◽  
Author(s):  
Zhendong Zhao ◽  
Rongdong Yin
Keyword(s):  
Neural Network ◽  
Vibration Prediction

Vibration prediction of pellet mills power transmission by artificial neural network

2017 ◽  
Vol 37 (4) ◽  
pp. 464-470 ◽  
Author(s):  
Milos Milovancevic ◽  
Vlastimir Nikolic ◽  
Nenad T. Pavlovic ◽  
Aleksandar Veg ◽  
Sanjin Troha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Power Transmission ◽  
Error Function ◽  
Vibration Monitoring ◽  
Gradient Descent Method ◽  
Content Type ◽  
Vibration Prediction ◽  
Artificial Neural

Purpose The purpose of this study is to establish a vibration prediction of pellet mills power transmission by artificial neural network. Vibration monitoring is an important task for any system to ensure safe operations. Improvement of control strategies is crucial for the vibration monitoring. Design/methodology/approach As predictive control is one of the options for the vibration monitoring in this paper, the predictive model for vibration monitoring was created. Findings Although the achieved prediction results were acceptable, there is need for more work to apply and test these results in real environment. Originality/value Artificial neural network (ANN) was implemented as the predictive model while extreme learning machine (ELM) and back propagation (BP) learning schemes were used as training algorithms for the ANN. BP learning algorithm minimizes the error function by using the gradient descent method. ELM training algorithm is based on selecting of the input weights randomly of the ANN network and the output weight of the network are determined analytically.

Vibration prediction of hardened steel in precision cutting based on BP neural network

2020 ◽  
Vol 37 (6) ◽  
pp. 305-312
Author(s):  
Guang-Jun Chen ◽  
Shuai Hou ◽  
Bing Yan ◽  
Ren-Ping Guo ◽  
Song-Xin Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Hardened Steel ◽  
Vibration Prediction

scholarly journals Effect of technological parameters on vibration acceleration in milling and vibration prediction with artificial neural networks

2019 ◽  
Vol 252 ◽  
pp. 03015 ◽  
Author(s):  
Ireneusz Zagórski ◽  
Monika Kulisz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Milling Process ◽  
Technological Parameters ◽  
Vibration Acceleration ◽  
Az91d Magnesium Alloy ◽  
Vibration Prediction ◽  
Artificial Neural ◽  
Network Software

This paper reports on the study of vibration acceleration in milling and vibration prediction by means of artificial neural networks. The milling process, carried out on AZ91D magnesium alloy with a PCD milling cutter, was monitored to observe the extent to which the change of selected technological parameters (vc, fz, ap) affects vibration acceleration ax, ay and az. The experimental data have shown a significant impact of technological parameters on maximum and RMS vibration acceleration. The simulation works employed the artificial neural networks modelled with Statistica Neural Network software. Two types of neural networks were employed: MLP (Multi-Layered Perceptron) and RBF (Radial Basis Function).

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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