Title :
Prediction of the Acoustic Pressure Backscattered by a Steel Tube Using Neural Networks Approach
Author :
Dariouchy, A. ; Aassif, E. ; Maze, G. ; Latif, R. ; Decultot, D. ; Laaboubi, M.
Author_Institution :
Univ. Ibn Zohr, Agadir
Abstract :
A new approach is used to predict the pressure backscattered by a tube using the artificial neural networks (ANNs) techniques. The studied tube consists of steel. During the development of the network, several configurations are evaluated for various radius ratio b/a (a: outer radius, b: inner radius of tube). The multilayer perceptron (MLP) is used in the current study. The optimal model selected is a network with one hidden layer. This model is able to predict the pressure backscattered with a mean relative error (MRE) of about a 1.6%. The comparison of the obtained and the experimental results indicate that the ANN method is suitable to be used to predict this one.
Keywords :
acoustic waves; backscatter; multilayer perceptrons; pipes; production engineering computing; steel; acoustic pressure backscattered; artificial neural network; mean relative error; multilayer perceptron; steel tube; Artificial neural networks; Geometry; Multilayer perceptrons; Neural networks; Predictive models; Resonance; Statistical analysis; Steel; Testing; Uncertainty; Multilayer perceptron; Neural network; Pressure backscattered; Steel tube;
Conference_Titel :
Computational Intelligence and Intelligent Informatics, 2007. ISCIII '07. International Symposium on
Conference_Location :
Agadir
Print_ISBN :
1-4244-1158-0
Electronic_ISBN :
1-4244-1158-0
DOI :
10.1109/ISCIII.2007.367373