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

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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.

### Keywords

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

### Acknowledgments

The data set used in this research work was collected as part of a project supervised by Dr. M. Kamel at the Aluminum factory in Egypt and analyzed by Dr. Aboabbas Hussain.

### Compliance with ethical standards

### Conflict of interest

Ali Rodan declares that he has no conflict of interest. Alaa F. Sheta declares that he has no conflict of interest. Hossam Faris declares that he has no conflict of interest.

### Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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