Soft Computing

, Volume 21, Issue 22, pp 6811–6824 | Cite as

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

Methodologies and Application

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.

Keywords

Recurrent neural network Reservoir computing Support vector machines Privileged information Winding machines Engineering process 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  2. 2.Computers and Systems DepartmentElectronics Research Institute (ERI)GizaEgypt

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