Neuro-Fuzzy Based Modeling of Electrostatic Fields for Non- Harmattan Season in Zaria

Abstract

This paper presents a Neuro-Fuzzy based modeling of electrostatic fields during non-harmattan season in Zaria, Nigeria based on the on-line data capturing mechanism, which involved the use of a data acquisition system interfaced with a digital electrostatic field strength meter (model 257D) and a computer system. The acquired electric field data are captured by the computer using the Microsoft Office Excel Program for twenty- four months (2007 - 2009). The focus of the analysis is determining the effect of environmental factors such as temperature, pressure and relative humidity on the static electric field during the non-harmattan season. The plots of the electrostatic field against the variation of the environmental factors were used as the qualitative analytical tools and yielded a non-linear relationship. The data was analyzed using Neuro-Fuzzy technique, which is a hybrid intelligent system combining the benefits of computational techniques of Fuzzy Logic and Artificial Neural Networks. The result of the analyses yielded good neural statistical values of Root Mean Square (RMS) of 0.35, Average Absolute Error of 0.23, and Pearson R value of 0.94 for the non-harmattan scenario, which are reflections of a good model. The result was further buttressed by the 3D plot of the neuro- Fuzzy based modeling of the experimental parameters. With the insignificant values of the RMS and Average Absolute value, the empirical model gave a good prediction which could be relied upon to predict the electrostatic fields during non-harmattan in Zaria, Nigeria.