Tools for Modelling and Identiﬁcation with Bond Graphs and Genetic Programming
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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.
Cite this work
Stefan Wiechula (2007). Tools for Modelling and Identiﬁcation with Bond Graphs and Genetic Programming. UWSpace. http://hdl.handle.net/10012/2739