Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

Author:

Abbasi Nozari Hasan,Dehghan Banadaki Hamed,Mokhtare Mohammad,Hekmati Vahed Somayeh

Publisher

Zhejiang University Press

Subject

General Engineering

Reference19 articles.

1. Babuska, R., Verbruggen, H., 2003. Neuro-fuzzy methods for nonlinear system identification. Ann. Rev. Control, 27(1): 73–85. [doi:10.1016/S1367-5788(03)00009-9]

2. Banadaki, H.D., Nozari, H.A., Kakahaji, H., 2011. Nonlinear simulator model identification of a walking beam furnace using recurrent local linear neuro-fuzzy network. Int. J. Control Autom., 4(4):123–134.

3. Bastogne, T., Noura, H., Sibille, P., Richard, A., 1998. Multivariable identification of a winding process by subspace methods for tension control. Control Eng. Pract., 6(9): 1077–1088.

4. Braatz, R.D., Ogunnaike, B.A., Featherstone, A.P., 1996. Identification, Estimation and Control of Sheet and Film Processes. 13th IFAC World Congress, p.319–324.

5. Ebler, N.A., Arnason, R., Michaelis, G., D’sa, N., 1993. Tension control: dancer rolls or load cells. IEEE Trans. Ind. Appl., 29(4):727–739. [doi:10.1109/28.231986]

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