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Workshop 3: Advanced computational intelligence techniques for identification, control and optimization of nonlinear systems

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2 Author(s)
Venayagamoorthy, G.K. ; University of Missouri-Rolla, USA ; Padhi, R.

Neural networks and fuzzy systems are natural candidates as approximators of a nonlinear time series or dynamical system, due to their intrinsic nonlinearity and computational simplicity. Under the stationarity hypothesis for the system generating the data, the NARX (Nonlinear Auto-Regressive with an eXogenous (X) variable) neural networks are able to solve the nonlinear identification problem. The multilayer feedforward and recurrent neural networks types are employed.

Published in:

Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, 2006 IEEE

Date of Conference:

4-6 Oct. 2006