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

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 it. To understand and predict signals, an intelligent approach is taken using ARNN model. The deterministic component is predicted by developing a neural network based non-linear autoregressive model and the error component by using a linear stochastic model. The final forecast is formed by combining the results from each of the models and evaluated using the mean square error results. Validation of the prediction is obtained through a comparison of the results with existing models. It shows that ARNN reduced the MSE error 14-36%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$%$$\end{document}, RMSS error 19-46%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$%$$\end{document} and NMSE 18-42%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$%$$\end{document} as compared to existing models. Moreover, ARNN model can be used for low as well as for high volatility data elements. The results show that the proposed model provides improved predictions and minimizes high dependence on design parameters with low computational cost.