20 research outputs found
Grey-box model identification via evolutionary computing
This paper presents an evolutionary grey-box model identification methodology that makes the best use of a priori knowledge on
a clear-box model with a global structural representation of the physical system under study, whilst incorporating accurate blackbox
models for immeasurable and local nonlinearities of a practical system. The evolutionary technique is applied to building
dominant structural identification with local parametric tuning without the need of a differentiable performance index in the
presence of noisy data. It is shown that the evolutionary technique provides an excellent fitting performance and is capable of
accommodating multiple objectives such as to examine the relationships between model complexity and fitting accuracy during the
model building process. Validation results show that the proposed method offers robust, uncluttered and accurate models for two
practical systems. It is expected that this type of grey-box models will accommodate many practical engineering systems for a better
modelling accuracy