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|Title:||Grey-box model identification via evolutionary computing|
|Authors:||Tan, K.C. |
|Source:||Tan, K.C., Li, Y. (2002-07). Grey-box model identification via evolutionary computing. Control Engineering Practice 10 (7) : 673-684. ScholarBank@NUS Repository. https://doi.org/10.1016/S0967-0661(02)00031-X|
|Abstract:||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 black-box 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. © 2002 Elsevier Science Ltd. All rights reserved.|
|Source Title:||Control Engineering Practice|
|Appears in Collections:||Staff Publications|
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