Neural Classification of h- and p-version Elements

Z  Goudarzi  and A  Abedian

doi:10.17485/ijst/2014/v7i5.5

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Neural Classification of h- and p-version Elements

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2014/v7i5.5

Year: 2014, Volume: 7, Issue: 5, Pages: 622–627

Original Article

Neural Classification of h- and p-version Elements

Z. Goudarzi¹ and A. Abedian^2*

1 Department of Computer Engineering, University of Isfahan, Isfahan, Iran.
2 Department of Mechanical Engineering, Daneshpajoohan Higher Education Institute, Isfahan, Iran; abedian@me.iut.ac.ir

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This paper deals with a comparative performance of traditional elements and high order elements, making use of their formulation as vectors (or patterns) in a multi-dimensional space of proper attributes. The classification can be carried out with the help a self-organizing feature map of Kohonen with the patterns corresponding to the input space. The work makes use of the four attributes: its number of nodes, number of Lejendre terms, maximum degree of interpolation polynomials and number of degrees of freedom per node, though a more general characterization is also possible.

Keywords: Classification, Finite Elements, Kohonen’s Network, Neural Networks