Uncertainty analysis of artificial neural network (ANN) aproximated function for experimental data using sequential perturbation method - Mohd Jukimi, Joni (2009)
This thesis describes a comparative study of uncertainty estimation for unknown function using sequential perturbation method with Artificial Neural Network (ANN) approximated function. The objective of this project is to propose a new technique in calculating uncertainty estimation for an unknown function which is data obtains from experimental or measurement. For this research of the uncertainty analysis can be applied to calculate uncertainty value for the experiment data that not have function. The process to determine uncertainty have six step including begin from selected experiment function, generate the experiment data, function approximation using ANN, calculate the uncertainty for analytical method manually, applied the sequential perturbation method with ANN and lastly determine percent error between sequential perturbation method with ANN compare with the analytical method. Meanwhile, the variation of uncertainty error for Sequential Perturbation method without ANN is 0.0510%, but the error of sequential perturbation method with The ANN is 0.1559%. Then compare the value of Sequential Perturbation (numerical) method with ANN and value of Analytical method to validate the data. The new technique will be approving to determine the uncertainty analysis using combination of Sequential Perturbation method with artificial neural network (ANN). Any experiment also can be use, the applications of Sequential Perturbation method with ANN propose in this study. Consequently it implies the application of Sequential Perturbation method is a good as the application of the analytical method in order to calculate the propagation of uncertainty.