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- W2034821216 abstract "Abstract In the present work we introduce a system identification framework where no a priori information on the system to be identified is available. Focusing in the specific case of radial basis functions neural networks models, we insert the choice of the model complexity and its inputs in the optimization procedure together with the model parameters, aiming at accuracy, model validity and regularization in a multiobjective approach. The multicriteria problem is solved by means of the multiobjective cuckoo search, which is based on an archiving technique and the crowding distance metric. Simulation results are promising when the methodology is applied to identify a robot arm given solely input and output data." @default.
- W2034821216 created "2016-06-24" @default.
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- W2034821216 date "2014-01-01" @default.
- W2034821216 modified "2023-09-26" @default.
- W2034821216 title "Multiobjective Cuckoo Search Applied to Radial Basis Function Neural Networks Training for System Identification" @default.
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- W2034821216 doi "https://doi.org/10.3182/20140824-6-za-1003.01249" @default.
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