DocumentCode
1991437
Title
Identification of discrete linear system in state space form using neural network
Author
Wang, Dali ; Zilouchian, Ali
Author_Institution
Dept. of Electr. Eng., Florida Atlantic Univ., Boca Raton, FL, USA
fYear
1998
fDate
2-4 Mar 1998
Firstpage
338
Lastpage
342
Abstract
A novel supervised recurrent neural network architecture for identification of discrete linear systems is introduced. The proposed neural network architecture directly provides the state space parameters of a given system based upon input-output data available. The simulation experiments demonstrate the effectiveness of the proposed method for both single input single output (SISO) and multi-input multi-output (MIMO) systems
Keywords
MIMO systems; discrete systems; identification; linear systems; recurrent neural nets; state-space methods; MIMO; SISO; discrete linear system; identification; input-output data; state space parameters; supervised recurrent neural network architecture; Electronic mail; Frequency domain analysis; Intelligent networks; Linear systems; MIMO; Neural networks; Neurons; Recurrent neural networks; State-space methods; System identification;
fLanguage
English
Publisher
ieee
Conference_Titel
Devices, Circuits and Systems, 1998. Proceedings of the 1998 Second IEEE International Caracas Conference on
Conference_Location
Isla de Margarita
Print_ISBN
0-7803-4434-0
Type
conf
DOI
10.1109/ICCDCS.1998.705860
Filename
705860
Link To Document