Title :
Identification of a nonlinear multi stand rolling system by a structured recurrent neural network
Author :
Hintz, Christian ; Rau, Martin ; Schröder, Dierk
Author_Institution :
Inst. for Electr. Drive Syst., Tech. Univ. Munchen, Germany
Abstract :
In this paper, the authors present an identification method for mechatronic systems consisting of a linear part with unknown parameters and an unknown nonlinearity (systems with an isolated nonlinearity). A structured recurrent neural network is used to identify the unknown parameters of the known signal flow chart. The novelty of this approach is the simultaneous identification of the parameters of the linear part and the nonlinearity. Prior structural and parameter knowledge are used in a natural way
Keywords :
identification; mechatronics; motion control; nonlinear control systems; process control; recurrent neural nets; rolling mills; identification method; known signal flow chart; mechatronic systems; nonlinear multi stand rolling system identification; parameter knowledge; structural knowledge; structured recurrent neural network; unknown parameters; Control nonlinearities; Couplings; Flowcharts; Mechatronics; Neural networks; Neurofeedback; Optimal control; Recurrent neural networks; Signal processing; State estimation;
Conference_Titel :
Industry Applications Conference, 2000. Conference Record of the 2000 IEEE
Conference_Location :
Rome
Print_ISBN :
0-7803-6401-5
DOI :
10.1109/IAS.2000.881972
