Ridge Polynomial Neural Network for Non-destructive Eddy Current Evaluation

  • Tarik Hacib
  • Yann Le Bihan
  • Mohammed Rachid Mekideche
  • Nassira Ferkha
Part of the Studies in Computational Intelligence book series (SCI, volume 327)


Motivated by the slow learning properties of Multi-Layer Perceptrons (MLP) which utilize computationally intensive training algorithms, such as the backpropagation learning algorithm, and can get trapped in local minima, this work deals with ridge Polynomial Neural Networks (RPNN), which maintain fast learning properties and powerful mapping capabilities of single layer High Order Neural Networks (HONN). The RPNN is constructed from a number of increasing orders of Pi-Sigma units, which are used to solving inverse problems in electromagnetic Non-Destructive Evaluation (NDE). The mentioned inverse problems were solved using Artificial Neural Network (ANN) for building polynomial functions to approximate the correlation between searched parameters and field distribution over the surface. The inversion methodology combines the RPNN network and the Finite Element Method (FEM). The RPNN are used as inverse models. FEM allows the generation of the data sets required by the RPNN parameter adjustment. A data set is constituted of input (normalized impedance, frequency) and output (lift-off and conductivity) pairs. In particular, this paper investigates a method for measurement the lift-off and the electrical conductivity of conductive workpiece. The results show the applicability of RPNN to solve non-destructive eddy current problems instead of using traditional iterative inversion methods which can be very time-consuming. RPNN results clearly demonstrate that the network generate higher profit returns with fast convergence on various noisy NDE signals.


Finite Element Method Inverse Problem Solution Eddy Current Problem Functional Link Neural Network Finite Element Method Solver 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tarik Hacib
    • 1
  • Yann Le Bihan
    • 2
  • Mohammed Rachid Mekideche
    • 1
  • Nassira Ferkha
    • 1
  1. 1.Laboratoire d’études et de modélisation en électrotechnique, Faculté des Sciences de l’IngénieurUniv. JijelJijelAlgérie
  2. 2.Laboratoire de Génie Electrique de ParisSUPELEC, UMR 8507 CNRS, UPMC Univ. Paris 06, Univ. Paris-Sud 11Gif-sur- Yvette CedexFrance

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