Tools for Modelling and Identification with Bond Graphs and Genetic Programming
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Date
2007-03-07T15:43:05Z
Authors
Wiechula, Stefan
Journal Title
Journal ISSN
Volume Title
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.