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Soft Computing

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24.06.2016 | Methodologies and Application

Bidirectional reservoir networks trained using SVM\(+\) privileged information for manufacturing process modeling

verfasst von: Ali Rodan, Alaa F. Sheta, Hossam Faris

Erschienen in: Soft Computing | Ausgabe 22/2017

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Abstract

In the last decade, a wide range of machine learning approaches were proposed and experimented to model highly nonlinear manufacturing processes. However, improving the performance of such models is challenging due to the complexity and high dimensionality of the manufacturing processes in general. In this paper, we propose bidirectional echo state reservoir networks (Bi-ESNs) trained using support vector machine privileged information method (SVM\(+\)) to model a winding machine process. The proposed model will be applied, tested and compared to reported models in the literature such as classical ESN with linear regression, ESN with a linear SVM readout, genetic programming, feedfoward neural network with backpropagation, radial basis function network, adaptive neural fuzzy inference system and local linear wavelet neural network. The developed results show that Bi-ESNs trained with SVM\(+\) are promising. It was able to provide better generalization performance compared to other models.

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Titel: Bidirectional reservoir networks trained using SVM privileged information for manufacturing process modeling
verfasst von: Ali Rodan
Alaa F. Sheta
Hossam Faris
Publikationsdatum: 24.06.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 22/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2232-9

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