Abstract
Two-phase neuro-system identification method is presented. The 1$^{st}$-phase identification uses conventional neural network mapping for modeling an input-output system. The 2$^{nd}$ -phase modeling is also performed sequentially using the 1$^{st}$-phase modeling errors. In the 2$^{nd}$ a phase modeling, newly generated input signals, which are obtained by summing the 1st-phase modeling error and artificially generated uniform series, are utilized as system's I-O mapping elements. The 1$^{st}$-phase identification is interpreted as a “Real Model” system identification because it uses system's real data(i.e., observations and control inputs) while the 2$^{nd}$ -phase identification as a “Artificial Model” identification because of using artificial data. Experimental results are given to verify that the two-phase neuro-system identification could reduce the overall modeling errors.rrors.
