Reference : Non-Linear Identification Using a Genetic Algorithm Approach for Model Selection
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Mechanical engineering
Non-Linear Identification Using a Genetic Algorithm Approach for Model Selection
Platten, Michael F [University of Manchester > School of Engineering > > >]
Wright, Jan Robert [University of Manchester > School of Engineering > > >]
Worden, Keith [University of Sheffield > Department of Mechanical Engineering > > >]
Cooper, Jonathan Edward [University of Manchester > School of Engineering > > >]
Dimitriadis, Grigorios mailto [Université de Liège - ULiège > Département d'aérospatiale et mécanique > Interactions Fluide-Structure - Aérodynamique expérimentale >]
Proceedings of the 23rd International Modal Analysis Conference
Bethel, CT
International Modal Analysis Conference IMAC 23
du 31 janvier au 3 fevrier 2005
Society for Experimental Mechanics
Orlando, Florida
[en] Nonlinear system identification ; Model selection ; Genetic Algorithms
[en] The Non-Linear Resonant Decay Method is an approach for the identification of non-linear systems with large
numbers of degrees of freedom. The identified non-linear model is expressed in linear modal space and
comprises the modal model of the underlying linear system with additional terms representing the non-linear
behaviour. Potentially, a large number of these non-linear terms will exist but not all of them will be significant.
The problem of deciding which and how many terms are required for an accurate identification has previously
been addressed using the Forward Selection and Backward Elimination techniques. In this paper, a Genetic
Algorithm optimisation is proposed as an alternative to those methods. A simulated 5-DOF lumped parameter
non-linear system is used to demonstrate the proposed optimisation. The use of separate data sets for the
identification and validation of the modal model is also investigated. It is found that the Genetic Algorithm
approach yields significantly better results than the Backward Elimination and Forward Selection algorithms in
many cases.
Researchers ; Professionals

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