Recurrent neural networks for fuzzy data

Issue title: Data Mining in Engineering

Guest editors: Rudolf Krusex, Michael Beery and Lotfi A. Zadehz

Article type: Research Article

Authors: Freitag, Steffen | Graf, Wolfgang^{; *} | Kaliske, Michael

Affiliations: Institute for Structural Analysis, Technische Universität Dresden, Dresden, Germany | [x] University of Magdeburg, Germany | [y] University of Liverpool, UK | [z] University of California at Berkeley, USA

Correspondence: [*] Corresponding author: Institute for Structural Analysis, Technische Universität Dresden, 01062 Dresden, Germany. Tel.: +49 351 463 34172; Fax: +49 351 463 37086; E-mail: [email protected].

Abstract: In this paper, a model-free approach for data mining in engineering is presented. The numerical approach is based on artificial neural networks. Recurrent neural networks for fuzzy data are developed to identify and predict complex dependencies from uncertain data. Uncertain structural processes obtained from measurements or numerical analyses are used to identify the time-dependent behavior of engineering structures. Structural action and response processes are treated as fuzzy processes. The identification of uncertain dependencies between structural action and response processes is realized by recurrent neural networks for fuzzy data. Algorithms for signal processing and network training are presented. The new recurrent neural network approach is verified by a fuzzy fractional rheological material model. An application for the identification and prediction of time-dependent structural behavior under dynamic loading is presented.

DOI: 10.3233/ICA-2011-0373

Journal: Integrated Computer-Aided Engineering, vol. 18, no. 3, pp. 265-280, 2011