DocumentCode :
2773563
Title :
Evaluation function optimization for the genetic algorithm based tuning of NN-ANARX model structure
Author :
Nomm, Sven ; Vassiljeva, Kristina ; Petlenkov, Eduard
Author_Institution :
Control Syst. Dept., Tallinn Univ. of Technol., Tallinn, Estonia
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
7
Abstract :
Present paper focuses its attention on the application of genetic algorithm to adjust the NN-ANARX type structure improving performance of the identified model. Namely constructive procedure is proposed to choose parameters of the multi-criteria fitness function. Whereas main goal of present research is to find optimal linear combination of three qualitative parameters: ODCCF based criteria, mean square error and model order, those parameters are commonly used to evaluate model performance and validity. Numeric values of the fitness function coefficients for the most common classes of nonlinear systems proposed as a secondary result of the research.
Keywords :
genetic algorithms; mean square error methods; neural nets; NN-ANARX model structure tuning; ODCCF; constructive procedure; evaluation function optimization; fitness function coefficients; genetic algorithm; mean square error; multicriteria fitness function; neural networks based additive nonlinear autoregressive exogenous; numeric values; optimal linear combination; Artificial neural networks; Biological cells; Encoding; Genetic algorithms; Mathematical model; Tuning; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
ISSN :
2161-4393
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
Type :
conf
DOI :
10.1109/IJCNN.2012.6252599
Filename :
6252599
Link To Document :
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