Abstract
The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the saturation magnetization prediction. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The correlation of the involved parameters with the growth process was examined by employing the central composite design method through designating set up experiments that will determine the interaction of the variables. The vibrating sample magnetometer (VSM) was used to confirm the statistical analysis. The ANFIS network was used to perform a variable search. Then, it was used to determine how 4 parameters (pH, Temperature (∘C), Time (min), Alkali rate of addition (mL.s−1)) influence saturation magnetization prediction. The results indicated that pH is the most influential to sugarcane growth prediction, and the best predictor of accuracy. The response obtained by VSM suggests that the saturation magnetization of nanomagnetite particles can be controlled by restricting the effective parameters.
Published Version
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