Abstract
The paper presents a prediction of non-linear exogenous signal by
optimized intelligent auto-regressive neural network model (ARNN). A signal
comprises of two sets of data called deterministic and error. The former type
of data represents the degradation index of a signal, while the error is the
uncertainties associated with the signal. To understand and predict signals, a
intelligent approach is taken through the use of ARNN model. In this approach,
the rst step is to diagnose whether a time series signal is normally distributed
or not by utilizing the Jarque-Bera test. The high and low volatility data ele-
ments can be separated via kurtosis hypothesis. The deterministic component
of the signal is also predicted by developing a neural network based non-linear
autoregressive model (NN-NARX) and the error component by using a linear
model. The nal forecast is formed by combining the results determined from
each of the models and evaluated using the mean square error results. Vali-
dation of the prediction is obtained through a comparison of the results with
other models such as ARNN, traditional ARMX, and NARX models. The re-
sults show that the proposed model provides improved predictions, minimize
high dependence on design parameters with low computational cost.