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Effect of Representation on the Performance of Neural Networks in Structural Engineering Applications
Author(s) -
Gunaratnam D. J.,
Gero J. S.
Publication year - 1994
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.1994.tb00365.x
Subject(s) - curse of dimensionality , computer science , exploit , representation (politics) , artificial neural network , domain (mathematical analysis) , artificial intelligence , machine learning , theoretical computer science , mathematics , mathematical analysis , politics , political science , law , computer security
The pattern‐mapping, pattern‐classification, and optimization capabilities of neural networks have been used to solve a number of structural analysis and design problems. Most applications exploit the pattern‐mapping capability and are based on the back‐propagation paradigm for neural networks. There are a number of factors that influence the performance of these networks. This paper initially discusses these factors and the domain‐dependent and ‐independent techniques presently available for improving performance. The paper then considers the effect of representation, selected for the input/output pattern pairs, on the performance of these networks and demonstrates that representations based on dimensionless terms, derived from dimensional analysis, lead to improved performance. It is shown that dimensional analysis provides a representational framework, with reduced dimensionality and embedded domain knowledge, within which effective learning can take place and that this representational change can be used to enhance the domain‐independent and ‐dependent techniques presently available for improving performance of these networks.