DocumentCode :
2618911
Title :
A new on-line identification approach for affine modeling of nonlinear processes using an adaptive neural network
Author :
Kamalabady, A. Sabet ; Salahshoor, K.
Author_Institution :
Dept. of Autom. & Instrum., Pet. Univ. of Technol., Tehran
fYear :
2008
fDate :
25-27 June 2008
Firstpage :
735
Lastpage :
740
Abstract :
This paper presents a new approach for on-line identification of an exact affine model for single-input, single- output (SISO) processes with nonlinear and time-varying behaviors. For this purpose, a modified growing and pruning algorithm for radial basis function (MGAP-RBF) neural network is used for affine modeling of the SISO nonlinear and time-varying processes. The extended Kalman filter (EKF) is utilized as an effective learning algorithm for parameter adaptation in the GAP-RBF neural network. The performances of the modified GAP-RBF and the original GAP-RBF are evaluated on a non-affine CSTR benchmark problem with nonlinear and time-varying behavior. Simulation results show good performance of the MGAP-RBF neural network for identification of an exact affine model for the CSTR.
Keywords :
Kalman filters; adaptive control; identification; neurocontrollers; nonlinear control systems; time-varying systems; adaptive neural network; affine modeling; extended Kalman filter; nonlinear behaviors; online identification approach; radial basis function; single-input single-output process; time-varying behaviors; Adaptive control; Adaptive systems; Automatic control; Automation; Continuous-stirred tank reactor; Mobile robots; Neural networks; Neurons; Performance evaluation; Programmable control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Automation, 2008 16th Mediterranean Conference on
Conference_Location :
Ajaccio
Print_ISBN :
978-1-4244-2504-4
Electronic_ISBN :
978-1-4244-2505-1
Type :
conf
DOI :
10.1109/MED.2008.4602154
Filename :
4602154
Link To Document :
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