Journal ArticleDOI
Modeling and classification of non-linear systems using neural networks--II. A preliminary experiment
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TLDR
In this article, a neural network is trained to distinguish between cracked and uncracked states of the beam when presented with measured time data, and it is shown that the dynamic behaviour is characteristic of a beam with an actual fatigue crack.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 1994-07-01. It has received 15 citations till now. The article focuses on the topics: Artificial neural network.read more
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Past, present and future of nonlinear system identification in structural dynamics
TL;DR: In this article, a review of the past and recent developments in system identification of nonlinear dynamical structures is presented, highlighting their assets and limitations and identifying future directions in this research area.
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Non-linear dynamics tools for the motion analysis and condition monitoring of robot joints
TL;DR: The results suggest a rise in unstable behaviour due to the introduction of backlash in robot joints, and a straightforward method for condition monitoring using non-linear dynamics characteristics, based on a classification procedure, is suggested.
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Fault location in a framework structure using neural networks
TL;DR: It is shown that a network trained on data from finite-element simulation of the structure can successfully locate faults in the framework itself.
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Damage detection on crates of beverages by artificial neural networks trained with finite-element data
TL;DR: Recognition of representative damages on returnable crates of beverages is carried out by an artificial neural network (ANN) trained exclusively with frequency response spectra from finite-element simulations, finding a loose coupling of two ANN yields best recognition results.
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Development and application of computational intelligence approaches for the identification of complex nonlinear systems
TL;DR: It is shown that the method of this paper provides a robust methodology for developing reduced-order, reduced-complexity, computational models (in the form of governing differential equations) that can be used for obtaining high-fidelity models that reflect the correct “physics” of the underlying phenomena.