Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
7-25-2012
Journal or Book Title
Smart Materials and Structures
Volume
21
Issue
11
First Page
1
Last Page
32
DOI
10.1088/0964-1726/21/11/115015
Abstract
We propose a novel type of neural networks for structural control, which comprises an adaptive input space. This feature is purposefully designed for sequential input selection during adaptive identification and control of nonlinear systems, which allows the input space to be organized dynamically, while the excitation is occurring. The neural network has the main advantages of (1) automating the input selection process for time series that are not known a priori; (2) adapting the representation to nonstationarities; and (3) using limited observations. The algorithm designed for the adaptive input space assumes local quasi-stationarity of the time series, and embeds local maps sequentially in a delay vector using the embedding theorem. The input space of the representation, which in our case is a wavelet neural network, is subsequently updated. We demonstrate that the neural net has the potential to significantly improve convergence of a black-box model in adaptive tracking of a nonlinear system. Its performance is further assessed in a full-scale simulation of an existing civil structure subjected to nonstationary excitations (wind and earthquakes), and shows the superiority of the proposed method.
Copyright Owner
IOP Publishing Ltd
Copyright Date
2012
Language
en
File Format
application/pdf
Recommended Citation
Laflamme, Simon; Slotine, J. J.E.; and Connor, J.J., "Self-organizing input space for control of structures" (2012). Civil, Construction and Environmental Engineering Publications. 76.
https://lib.dr.iastate.edu/ccee_pubs/76
Included in
Civil Engineering Commons, Construction Engineering and Management Commons, Environmental Engineering Commons, Structural Engineering Commons
Comments
This is a manuscript from an article from Smart Materials and Structures,21(115015)2012; 1-16. Doi: 10.1088/0964-1726/21/11/115015. Posted with permission.