Abstract
The paper presents a new version of a GMDH type algorithm able to perform an automatic model structure synthesis, robust model parameter estimation and model validation in presence of outliers. This algorithm allows controlling the complexity – number and maximal power of terms – in the models and provides stable results and computational efficiency. The performance of this algorithm is demonstrated on artificial and real data sets. As an example we present an application to the study of the association between clinical symptoms of Parkinsons disease and temporal patterns of neuronal activity recorded in the subthalamic nucleus of human patients.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Madala, H., Ivakhnenko, A.: Inductive Learning Algorithms for Complex Systems Modeling. CRC Press Inc., Boca Raton (1994)
Aksenova, T.I., Volkovych, V., Tetko, I.: Robust Polynomial Neural Networks in Quantative-Structure Activity Relationship Studies. SAMS 43, 1331–1341 (2003)
Yurachkovsky, Y.: Restoration of polynomial dependencies using self-organization. Soviet Automatic Control 14, 17–22 (1981)
Yurachkovsky, Y.: Convergence of multilayer algorithms of the Group Method of Data Handling. Soviet Automatic Control 14, 29–35 (1981)
Huber, P.: Robust Statistics. John Wiley & Sons Inc, Chichester (2003)
Hampel, F., Ronchetti, E.M., Rousseeuw, P., Stahel, W.: Robust Statistics: The Approach Based on Influence Function. John Wiley & Sons Inc., Chichester (2005)
Chibirova, O., Aksenova, T.I., Benabid, A., Chabardes, S., Larouche, S., Rouat, J., Villa, A.: Unsupervised Spike Sorting of extracellular electrophysiological recording in subthalamic nucleus parkinsonian patients. Biosystems 79, 59–71 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aksenova, T., Volkovich, V., Villa, A.E.P. (2005). Robust Structural Modeling and Outlier Detection with GMDH-Type Polynomial Neural Networks. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_139
Download citation
DOI: https://doi.org/10.1007/11550907_139
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
Online ISBN: 978-3-540-28756-8
eBook Packages: Computer ScienceComputer Science (R0)