Tools for Modelling and Identification with Bond Graphs and Genetic Programming
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Date
2007-03-07T15:43:05Z
Authors
Wiechula, Stefan
Advisor
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Publisher
University of Waterloo
Abstract
The contributions of this work include genetic programming grammars for
bond graph modelling and for direct symbolic regression of sets of differential
equations; a bond graph modelling library suitable for programmatic use; a
symbolic algebra library specialized to this use and capable of, among other
things, breaking algebraic loops in equation sets extracted from linear bond
graph models. Several non-linear multi-body mechanics examples are pre-
sented, showing that the bond graph modelling library exhibits well-behaved
simulation results. Symbolic equations in a reduced form are produced au-
tomatically from bond graph models. The genetic programming system is
tested against a static non-linear function identification problem using type-
less symbolic regression. The direct symbolic regression grammar is shown
to have a non-deceptive fitness landscape: perturbations of an exact pro-
gram have decreasing fitness with increasing distance from the ideal. The
planned integration of bond graphs with genetic programming for use as a
system identification technique was not successfully completed. A catego-
rized overview of other modelling and identification techniques is included as
context for the choice of bond graphs and genetic programming.