Inverse problem of aircraft structural parameter identification: application of genetic algorithms compared with artificial neural networks

Trivailo, P, Gilbert, T, Glessich, E and Sgarioto, D 2005, 'Inverse problem of aircraft structural parameter identification: application of genetic algorithms compared with artificial neural networks', in M. J. Colaco et al. (ed.) Proceedings of the Inverse Problems, Design and Optimization Symposium, Rio de Janeiro, 29 September 2006.


Document type: Conference Paper
Collection: Conference Papers

Title Inverse problem of aircraft structural parameter identification: application of genetic algorithms compared with artificial neural networks
Author(s) Trivailo, P
Gilbert, T
Glessich, E
Sgarioto, D
Year 2005
Conference name Inverse Problems, Design and Optimization Symposium
Conference location Rio de Janeiro
Conference dates 29 September 2006
Proceedings title Proceedings of the Inverse Problems, Design and Optimization Symposium
Editor(s) M. J. Colaco et al.
Publisher e-Papers Publishing House
Place of publication Rio de Janeiro
Abstract Recent advances in the asymptotic resource costs of pattern matching with compressed suffix arrays are attractive, but a key rival structure, the compressed inverted file, has been dismissed or ignored in papers presenting the new structures. In this paper we examine the resource requirements of compressed suffix array algorithms against compressed inverted file data structures for general pattern matching in genomic and English texts. In both cases, the inverted file indexes q-grams, thus allowing full pattern matching capabilities, rather than simple word based search, making their functionality equivalent to the compressed suffix array structures. When using equivalent memory for the two structures, inverted files are faster at reporting the location of patterns when the number of occurrences of the patterns is high.
Subjects Neural, Evolutionary and Fuzzy Computation
Keyword(s) inverse problems
aircraft structural parameter estimation
genetic algorithm
neural networks
DOI - identifier 10.1007/11880561_11
Copyright notice © Springer-Verlag Berlin Heidelberg 2006
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