Nonlinear dynamic identification using supervised neural gas algorithm

The dynamic identification of a nonlinear plant is not a trivial issue. The application of a neural gas network that is trained with a supervised batch version of the algorithm can produce identification models in a robust way. In this paper, the neural model identifies each local transfer function...

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Bibliographic Details
Published in2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) pp. 1 - 7
Main Authors Machon-Gonzalez, Ivan, Lopez-Garcia, Hilario
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2017
Subjects
Approximation algorithms
dynamics identification
Heuristic algorithms
Modeling
neural gas
Neurons
nonlinear
Prototypes
Training
transfer function
Transfer functions
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DOI10.1109/WSOM.2017.8020031

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Summary:The dynamic identification of a nonlinear plant is not a trivial issue. The application of a neural gas network that is trained with a supervised batch version of the algorithm can produce identification models in a robust way. In this paper, the neural model identifies each local transfer function demonstrating that the local linear approximation can be done. Moreover, other parameters are analyzed in order to obtain a correct modeling.
DOI:10.1109/WSOM.2017.8020031