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Proceedings Paper

More transparent neural network approach for modeling nonlinear hysteretic systems
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Paper Abstract

A powerful Volterra/Wiener Neural Network (VWNN) is designed to reflect the underlying dynamics of hysteretic systems. The nonlinear response of multi-degree-of-freedom systems subjected to force excitation can be tracked using this neural network. More importantly, the inner-workings of the network, such as the design parameters as well as the weights and biases, can be loosely related to physical properties of dynamic systems. This effort differs markedly from what is typically done for neural networks as well as the original version of the VWNN in Ref. 1. An adaptive training algorithm and improved formulation of high-order nodes are adopted to enable fast training and stable convergence. A training example is provided to demonstrate that the VWNN is able to yield a unique set of solutions (i.e., the weights) when the values of the controlling design parameters are fixed a priori. The selection of these design parameters in practical applications is discussed. The advantages of the VWNN illustrate the potential of applying highly flexible nonparametric identification techniques in a parametric fashion to suit the needs of structural health monitoring and damage detections.

Paper Details

Date Published: 18 August 2003
PDF: 8 pages
Proc. SPIE 5057, Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures, (18 August 2003); doi: 10.1117/12.482697
Show Author Affiliations
Jin-Song Pei, Univ. of Oklahoma (United States)
Andrew W. Smyth, Columbia Univ. (United States)

Published in SPIE Proceedings Vol. 5057:
Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures
Shih-Chi Liu, Editor(s)

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