Neural Network Inverse Method for Dielectric Structures by Multifrequency Signals

2006 ◽  
Author(s):  
O. Drobakhin ◽  
A.V. Doronin
Keyword(s):  
Neural Network ◽  
Inverse Method ◽  
Dielectric Structures

Material properties identification of a piezoelectric beam using inverse method

2019 ◽  
Vol 234 (7) ◽  
pp. 1351-1365 ◽  
Author(s):  
Mohammad Amin Nematollahi ◽  
Behzad Hasanshahi ◽  
Malihe Eftekhari ◽  
Ali Akbar Safavi
Keyword(s):  
Neural Network ◽  
Material Properties ◽  
Input Data ◽  
Inverse Method ◽  
Identification Problem ◽  
Mode Analysis ◽  
Network Training ◽  
Piezoelectric Beam ◽  
Actuation Mode ◽  
Identification Analysis

This paper presents an inverse method for material properties identification of a piezoelectric beam (piezoelectric charge and relative dielectric coefficients) using a wavelet-based neural network as an inverse tool. The identification analysis is carried out by using two approaches. In the first approach, i.e. sensor mode analysis, the input data for wavelet-based neural network training are measured voltages at several specific points on the beam's top surface resulting from the applied beam tip deflection. In the second approach, i.e. actuation mode analysis, the input data are values of the beam tip deflection caused by applying voltage on the beam's top surface. In this study, the input parameters employed to train the wavelet-based neural network are obtained using the finite element method. The identification results are compared with those of some conventional neural networks including radial basis function and multilayer perceptron. The results show that the proposed neural network is an efficient tool in the material properties identification problem.

A Comparative Study on Modeling of High Temperature Rheological Behavior of AZ80 Magnesium Alloy Using Artificial Neural Network and Inverse Method

2012 ◽  
Vol 522 ◽  
pp. 136-141
Author(s):  
Yan Lou ◽  
Luo Xing Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Temperature ◽  
Strain Rate ◽  
Rheological Behavior ◽  
Inverse Method ◽  
Strain Rate Range ◽  
Compression Tests ◽  
Prediction Ability ◽  
Artificial Neural

Artificial neural network (ANN) and inverse method were employed in modeling the rheological behavior of the AZ80 magnesium. The hot deformation behavior of extruded AZ80 magnesium was investigated by compression tests in the temperature 350-450 and strain rate range 0.01-50 s-1. Investigation of flow stress curves and microstructure of the compression specimen illustrate occurrence of dynamic recrystallization. The inverse method of non-liner regression was used to determine the parameters of the suggested constitutive equation. The maximum relative errors at different temperatures and different strain rates between experimental and predicted flow stresses by ANN and inverse method were compared. The results show the ANN derives statistical models have better similar prediction ability to those of inverse method, especially at high strain rate. This indicates that ANN can be used as an alternative modeling tool for high temperature rheological behavior studies.

CHAOTIC MEMORY DYNAMICS IN A RECURRENT NEURAL NETWORK WITH CYCLE MEMORIES EMBEDDED BY PSEUDO-INVERSE METHOD

1995 ◽  
Vol 05 (04) ◽  
pp. 1205-1212 ◽  
Author(s):  
SHIGETOSHI NARA ◽  
PETER DAVIS ◽  
MASAYOSHI KAWACHI ◽  
HIROO TOTSUJI
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Inverse Method ◽  
Memory Search ◽  
Neural Connectivity ◽  
Pseudo Inverse

It is shown that hierarchical bifurcation of chaotic intermittency among memories can be induced by reducing neural connectivity when sequences of similar patterns are stored in a recurrent neural network using the pseudo-inverse method. This chaos is potentially useful for memory search and synthesis.

A general inverse method for modelling extensional sedimentary basins

2000 ◽  
Vol 12 (3-4) ◽  
pp. 219-226 ◽  
Author(s):  
P. Bellingham ◽  
N. White
Keyword(s):  
Inverse Method ◽  
Sedimentary Basins
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