Two different partially recurrent neural net works structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure. The one partially recurrent neural network has feedback of a displacement component from the output layer to a tapped-delay-line (TDL) input layer. The other recurrent neural network based on the Innovation State Space model (ISSM) has feedback of the state space vector from the output layer to the input layer. The recurrent neural network approaches are validated with respect to prediction and simulation of a non-linear process by application to simulated data from a viscous damped oscillator with hysteresis of the curve-linear type described by the Bouc-Wen model. The oscillator is subjected to amplitude modulated Gaussian white noise filtered through a Kanai-Tajimi filter. It is found that the two neural network models can act as actual system identifiers, predictors and simulators. The recurrent neural network with a TDL seems to be a better simulator than the ISSM network.

purchase the full-text of this paper (price £20)

go to the previous paper
go to the next paper
return to the table of contents
return to the book description
purchase this book (price £55 +P&P)