Modeling of Direction-Dependent Processes Using Wiener Models and Neural Networks With Nonlinear Output Error Structure
Ai Hui Tan, Keith R. Godfrey · IEEE Transactions on Instrumentation and Measurement · 2004
The modeling of direction-dependent dynamic processes using Wiener models and recurrent neural network models with nonlinear output error structure is considered. The results obtained are compared for several simulated first-order and second-order processes and using three different types of input signals: a pseudorandom binary signal, an inverse-repeat pseudorandom binary signal and a multisine (sum of harmonics) signal. Experimental results on a real system, namely an electronic nose system, are also presented to illustrate the applicability of the techniques discussed.