Title :
Identification of nonlinear dynamic systems with recurrent neural networks and Kalman filter methods
Author :
Straub, Sebastian ; Schröder, Dierk
Author_Institution :
Inst. for Electr. Drives, Tech. Univ. Munchen, Germany
Abstract :
Identification of nonlinear dynamic systems has been the topic of many research projects in recent years. At this moment no uniform method to solve this problem exists. In this paper a new approach of identifying nonlinear dynamic systems is presented. It is based on the use of a General Regression Neural Network (GRNN), the parameters of which are trained by the Extended Kalman Filter Method. This strategy can be used in systems, in which not all states are accessible, and was analysed for the nonlinear behaviour of the roll-bite in rolling mills
Keywords :
Kalman filters; identification; metallurgical industries; nonlinear control systems; nonlinear dynamical systems; process control; recurrent neural nets; rolling mills; extended Kalman filter methods; general regression neural network; nonlinear behaviour; nonlinear dynamic systems; recurrent neural networks; rolling mills; system identification; Concrete; Control systems; Differential equations; Electrical equipment industry; Linear systems; Milling machines; Neural networks; Nonlinear dynamical systems; Nonlinear systems; Recurrent neural networks;
Conference_Titel :
Circuits and Systems, 1996. ISCAS '96., Connecting the World., 1996 IEEE International Symposium on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-3073-0
DOI :
10.1109/ISCAS.1996.541603
