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Neural Networks for Rapid Design and Analysis (1998)
BibTeX
@MISC{Sparks98neuralnetworks,
author = {Dean W. Sparks and Jr. and Peiman G. Maghami},
title = {Neural Networks for Rapid Design and Analysis},
year = {1998}
}
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Abstract
Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place. Introduction The...
Keyphrases
neural network artificial neural network rapid design dynamic component potential speed recurrent network state feedback control analysis time delay recursive nature appropriate number flexible system good approximation efficient dynamic judicious use overall time response analysis process spacecraft reaction wheel system analysis process nonlinear dynamic component prescribed input range simulation model
