Model Updating Using Neural Networks
Abstract
Accurate models are necessary in critical
applications. Key parameters in dynamic
systems often change during their life cycle due
to repair and replacement of parts or en-
vironmental changes. This dissertation presents
a new approach to update system models,
accounting for these changes. The approach
uses frequency domain data and a neural net-
work to produce estimates of the parameters
being updated, yielding a model representative
of the measured data. Current iterative methods
developed to solve the model updating problem
rely on min- imization techniques to nd the set
of model parameters that yield the best match
between experimental and analytical responses.
Since the minimization procedure requires a fair
amount of computation time, it makes the
existing techniques infeasible for use as part of
an adaptive control scheme correcting the
model parameters as the system changes. They
also require either mode shape expansion or
model reduction before they can be applied,
introducing errors in the procedure.
Furthermore, none of the existing techniques
has been applied to nonlinear systems. The
neural network estimates the parameters being
updated quickly and accurately without the
need to measure all degrees of freedom of the
system. This avoids the use of mode shape
expansion or model reduction techniques, and
allows for its implementation as part of an
adaptive control scheme. The proposed
technique is also capable of updating weakly
nonlinear systems. Numerical simulations and
experimental results show that the proposed
method has good accuracy and generalization
properties, and it is therefore, a suitable
alternative for the solution of the model
updating problem of this class of systems.
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- Doctoral Dissertations [13025]