Neural networks are powerful tools for black box system identification. However, their main drawback is the large number of parameters usually required to deal with complex systems. Classically, the model's parameters minimize a L2-norm-based criterion. However, when using strongly corrupted data, namely, outliers, the L2-norm-based estimation algorithms become ineffective. In order to deal with outliers and the model's complexity, the main contribution of this paper is to propose a robust system identification methodology providing neuromodels with a convenient balance between simplicity and accuracy. The estimation robustness is ensured by means of the Huberian function. Simplicity and accuracy are achieved by a dedicated neural network design based on a recurrent three-layer architecture and an efficient model order reduction procedure proposed in a previous work (Romero-Ugalde et al., 2013, “Neural Network Design and Model Reduction Approach for Black Box Nonlinear System Identification With Reduced Number of Parameters,” Neurocomputing, 101, pp. 170–180). Validation is done using real data, measured on a piezoelectric actuator, containing strong natural outliers in the output data due to its microdisplacements. Comparisons with others black box system identification methods, including a previous work (Corbier and Carmona, 2015, “Extension of the Tuning Constant in the Huber's Function for Robust Modeling of Piezoelectric Systems,” Int. J. Adapt. Control Signal Process., 29(8), pp. 1008–1023) where a pseudolinear model was used to identify the same piezoelectric system, show the relevance of the proposed robust estimation method leading balanced simplicity-accuracy neuromodels.
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May 2016
Research-Article
Robust Estimation of Balanced Simplicity-Accuracy Neural Networks-Based Models
Hector M. Romero Ugalde,
Hector M. Romero Ugalde
INSERM,
U1099;
U1099;
Search for other works by this author on:
Christophe Corbier
Christophe Corbier
Network and Telecommunications,
Université de Lyon,
Saint Etienne F-42023, France;
Université de Lyon,
Saint Etienne F-42023, France;
Network and Telecommunications,
Université de Saint Etienne,
Jean Monnet,
Saint-Etienne F-42000, France;
Université de Saint Etienne,
Jean Monnet,
Saint-Etienne F-42000, France;
LASPI,
IUT de Roanne F-42334, France
IUT de Roanne F-42334, France
Search for other works by this author on:
Hector M. Romero Ugalde
INSERM,
U1099;
U1099;
Christophe Corbier
Network and Telecommunications,
Université de Lyon,
Saint Etienne F-42023, France;
Université de Lyon,
Saint Etienne F-42023, France;
Network and Telecommunications,
Université de Saint Etienne,
Jean Monnet,
Saint-Etienne F-42000, France;
Université de Saint Etienne,
Jean Monnet,
Saint-Etienne F-42000, France;
LASPI,
IUT de Roanne F-42334, France
IUT de Roanne F-42334, France
1Corresponding author.
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received September 2, 2015; final manuscript received January 17, 2016; published online March 3, 2016. Assoc. Editor: Ryozo Nagamune.
J. Dyn. Sys., Meas., Control. May 2016, 138(5): 051001 (8 pages)
Published Online: March 3, 2016
Article history
Received:
September 2, 2015
Revised:
January 17, 2016
Citation
Romero Ugalde, H. M., and Corbier, C. (March 3, 2016). "Robust Estimation of Balanced Simplicity-Accuracy Neural Networks-Based Models." ASME. J. Dyn. Sys., Meas., Control. May 2016; 138(5): 051001. https://doi.org/10.1115/1.4032687
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