Title :
Nonlinear system identification using embedded dynamic neural networks
Author :
Yazdizadeh, A. ; Khorasani, K.
Author_Institution :
Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que., Canada
Abstract :
Identification of a class of nonlinear systems by using two neuro-dynamic structures is addressed. The capabilities of the proposed structures for representing the class of system considered are shown analytically. Selection criteria for specifying the fixed structural parameters and adaptation laws for updating the adjustable parameters are provided. Numerical simulation results are also provided to illustrate the performance of the proposed structures
Keywords :
feedforward neural nets; identification; multilayer perceptrons; nonlinear systems; adaptation laws; embedded dynamic neural networks; fixed structural parameters; neuro-dynamic structures; nonlinear system identification; Delay effects; Filters; Joining processes; Neural networks; Neurons; Nonhomogeneous media; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.682296
