Comparison Of Crossover In Genetic Algorithm For Discrete-Time System Identification
System identification is a process where a mathematical model is derived in order to explain dynamical behaviour of a system. One of its step is model structure selection and it is crucial that, in this step, an adequate model i.e. a model with a good balance between parsimony and accuracy of the mo...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Praise Worthy Prize
2021
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| Online Access: | http://eprints.utem.edu.my/id/eprint/25550/2/19726-45002-1-PB%20IREME%20%281%29.PDF http://eprints.utem.edu.my/id/eprint/25550/ https://www.praiseworthyprize.org/jsm/index.php?journal=ireme&page=article&op=view&path%5B%5D=25147 |
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| Summary: | System identification is a process where a mathematical model is derived in order to explain dynamical behaviour of a system. One of its step is model structure selection and it is crucial that, in this step, an adequate model i.e. a model with a good balance between parsimony and accuracy of the model is selected in approximating the system. Genetic algorithm (GA), a method known for optimisation is used for selecting a model structure. GA is known to be able to reduce much computational burden. This paper investigates the effect of different types of crossover, namely, single-point, double-point, multiple-point and uniform crossover, within GA in producing an optimum model structure for system identification. This was carried out using a computational software on a number of simulated data. As a conclusion, using Akaike Information Criterion as objective function, single point crossover produces the model with the best balance in most of the tests. |
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