Optimized Intelligent Auto-Regressive Neural Network Model (ARNN) for Prediction of Non-Linear Exogenous Signals

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.