Acoustic analysis and Modeling of the Group and phase Velocities of an Acoustic circumferential waves by an Adaptative Neuro-Fuzzy Inference System (ANFIS)

In this work, an Adaptative Neuro-Fuzzy Inference System (ANFIS) is applied to predict the velocity dispersion curves of the antisymmetric (A1) circumferential waves propagating around an elastic cooper cylindrical shell of various radius ratio b/a (a: outer radius and b: inner radius) for an infinite length cylindrical shell excited perpendicularly to its axis. The group and phase velocities, are determined from the values calculated using the eigenmode theory of resonances. These data are used to train and to test the performances of these models. This technique is able to model and to predict the group and phase velocities, of the anti-symmetric circumferential waves, with a high precision, based on different estimation errors such as mean relative error (MRE), mean absolute error (MAE) and standard error (SE). A good agreement is obtained between the output values predicted using ANFIS model and those computed by the eigenmode theory. It is found that the ANFIS networks are good tools for simulation and prediction of some parameters that carry most of the information available from the response of the shell. Such parameters may be found from the velocity dispersion of the circumferential waves, since it is directly related to the geometry and to the physical properties of the target

PREDICTION OF THE GROUP VELOCITY OF ACOUSTIC CIRCUMFERENTIAL WAVES BY ARTIFICIAL NEURAL NETWORK

International audienceThe present study investigates the use an Artificial Neural Network (ANN) to predict the velocity dispersion curve of the antisymmetric (A 1) circumferential waves propagating around an elastic cooper cylindrical shell of various radius ratio b/a (a: outer radius and b: inner radius) for an infinite length cylindrical shell excited perpendicularly to its axis. The group velocity is determined from the values calculated using the eigen mode theory of resonances. These data are used to train and to test the performances of this model. Levenberg-Marquaedt backpropagation training algorithm with tangent sigmoid transfer function and linear transfer function results in best model for prediction of group velocity. The overall regression coefficient, mean relative error (MRE), mean absolute error (MAE) and standard error (SE) are 1, 0.01%, 0.38 and 0.07. It is found that the neural networks are good tools for simulation and prediction of some parameters that carry most of the information available from the response of the shell. Such parameters may be found from the velocity dispersion of the circumferential waves, since it is directly related to the geometry and to the physical properties of the target

Modeling the Cut-off Frequency of Acoustic Signal with a Fuzzy Logic System

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Modeling the Cut-off Frequency of Acoustic Signal with a Fuzzy Logic System

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Measured and predicted of the longitudinal and transverse velocities of tube material using the Wigner-Ville and fuzzy logic techniques

International audienceIntelligent modelling tools as artificial neural network (ANN) and fuzzy logic approach are demonstrated to be competent when applied individuality to a variety of problems such as modelling and prediction. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. The advantage of using neuro-fuzzy (ANFIS) in this study for field modelling is given by the flexibility to adapt and relies on observed data rather than on analytical model of the system that some once it is difficult to establish it. In this work, we applied the fuzzy logic for modelling, measuring and predicting of the longitudinal and transverse velocities of material constituting the tube. The useful data to train and to test the performances of the model are determined from the values calculated trajectories of the proper modes theory of resonances and those extracted from time-frequency representations of Wigner-Ville. This representation is applied of the acoustic signal backscattered by an aluminium cylindrical shell immersed in water. The obtained values of the longitudinal and transverse velocities of material tube are in good agreement with those given in the scientific literature

Identification of circumferential acoustic waves propagating around the tube by multiresolution analysis and time-frequency representation

International audienceThis paper describes a technique based on Multiresolution Analysis (MRA) of the wavelet transform. This technique is applied for decomposition of the original acoustic signal backscattered by a thin tube. The Multiresolution technique was used as a tool to filter the wave modes contained in the original signal. The time-frequency representation using the Smoothed Pseudo-Wigner-Ville (SPWV) distribution is applied on the decomposed acoustic signal. The results obtained show that this technique of the Multiresolution analysis can identify not only single circumferential wave mode but also multimode effectively. This methodology permits to obtain the interesting results