Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification
Publication: Journal of Structural Engineering
Volume 132, Issue 1
Abstract
A new dynamic time-delay fuzzy wavelet neural network model is presented for nonparametric identification of structures using the nonlinear autoregressive moving average with exogenous inputs approach. The model is based on the integration of four different computing concepts: dynamic time delay neural network, wavelet, fuzzy logic, and the reconstructed state space concept from the chaos theory. Noise in the signals is removed using the discrete wavelet packet transform method. In order to preserve the dynamics of time series, the reconstructed state space concept from the chaos theory is employed to construct the input vector. In addition to denoising, wavelets are employed in combination with two soft computing techniques, neural networks and fuzzy logic, to create a new pattern recognition model to capture the characteristics of the time series sensor data accurately and efficiently. The model balances the global and local influences of the training data and incorporates the imprecision existing in the sensor data effectively. Experimental results on a five-story steel frame are employed to validate the computational model and demonstrate its accuracy and efficiency.
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Acknowledgment
The data used to train and validate the new computational model was provided by Professor Shih-Lin Hung of National Chiao Tung University and National Center Research on Earthquake Engineering (NCREE), Republic of China, which is gratefully acknowledged.
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Information & Authors
Information
Published In
Journal of Structural Engineering
Volume 132 • Issue 1 • January 2006
Pages: 102 - 111
Copyright
© 2006 ASCE.
History
Received: Apr 26, 2004
Accepted: Apr 20, 2005
Published online: Jan 1, 2006
Published in print: Jan 2006
Notes
Note. Associate Editor: Elisa D. Sotelino
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