Abstract
System identification (SI) is referred to as the procedure of building mathematical models for the dynamic systems using the measured data. Several modeling methods and types of models were studied by classifying SI in different ways, such as (1) black box, gray box, and white box; (2) parametric and non-parametric; and (3) linear SI, nonlinear SI, and evolutionary SI. A study of the literature also reveals that extensive focus has been paid to computational intelligence methods for modeling the output variables of the systems because of their ability to formulate the models based only on data obtained from the system. It was also learned that by embedding the features of several methods from different fields of SI into a given method, it is possible to improve its generalization ability. Popular variants of genetic programming such as multi-gene genetic programming is suggested as an alternative approach with its four shortcomings discussed as future aspects in paving way for evolutionary system identification.
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This study was supported by Shantou University Scientific Research Funded Project (Grant No. NTF 16002)
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Garg, A., Tai, K., Panda, B.N. (2017). System Identification: Survey on Modeling Methods and Models. In: Dash, S., Vijayakumar, K., Panigrahi, B., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-10-3174-8_51
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