Title :
Multimodel neural networks identification and failure detection of nonlinear systems
Author :
Selmic, Rastko R. ; Lewis, Frank L.
Author_Institution :
Signalogic Inc., Dallas, TX, USA
Abstract :
Multimodel identification and failure detection using neural networks (NN) is presented. It is an extension and application of nonlinear system identification using radial basis function NN. The state estimation error is proven to converge to zero asymptotically. Parameters of the identifier converge to the ideal parameters provided that persistency of excitation condition is fulfilled. Multiple model identification structure is analyzed, and its application to the multimodel failure detection is considered. Two simulation examples for NN identifiers are given. Simulation for intelligent multimodel failure detection using multi-neural networks identifiers is presented
Keywords :
fault diagnosis; nonlinear systems; parameter estimation; radial basis function networks; state estimation; RBF neural networks; excitation condition; failure detection; identification; multimodel failure detection; nonlinear system; parameter estimation; radial basis function networks; state estimation; Control system synthesis; Control systems; Equations; Neural networks; Neurons; Nonlinear control systems; Nonlinear systems; Robotics and automation; Signal processing; State estimation;
Conference_Titel :
Decision and Control, 2001. Proceedings of the 40th IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-7061-9
DOI :
10.1109/.2001.980299
