Journal
SOFT COMPUTING
Volume 21, Issue 22, Pages 6811-6824Publisher
SPRINGER
DOI: 10.1007/s00500-016-2232-9
Keywords
Recurrent neural network; Reservoir computing; Support vector machines; Privileged information; Winding machines; Engineering process
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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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