Uncertainty Analysis For The Unknown Function Using Artificial Neural Network Ann Approximated Function
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Uncertainty Analysis for the Unknown Function Using Artificial Neural Network (ANN) Approximated Function
This thesis deals with the finding of uncertainty analysis for the unknown function from experimental data by using Neural Network Approximation. The objective of this thesis is to estimates the uncertainty value for the unknown function where Artificial Neural Network (ANN) approximated function join together with sequential perturbation method will be applied. The thesis describes the uncertainty analysis techniques which are analytical (Newton Approximation) method and numerical (Sequential Perturbation) method to predict the uncertainty value and build up the new function from the experimental data via Fortran program using non-linear regression. The approach in analyzing uncertainty of Nusselt number is approximate the function via ANN using feed-forward and backpropagation network with four inputs and output were randomly generated. Finally, uncertainty outcome through sequential perturbation with ANN will be compare with the outcome using analytical method. Percentage error between both methods shall be compute to prove that uncertainty analysis for unknown function using sequential perturbation with ANN can also be use. From the results, average percentage error between Newton approximation (analytical method) and sequential perturbation (numerical method) retrieved is 5.52395×10-4 %. Meanwhile, the average percentage error between actual Nusselt number produced and approximated Nusselt number is 0.955373 %. However the main focus of this study is to determine whether sequential perturbation with ANN approximated function can be apply or not to estimate the uncertainty for the unknown function. The average percentage error between sequential perturbation with ANN and Newton approximation (analytical method) is 3.563%. Therefore, the objective is achieved.
Uncertainty Propagation Analysis of Artificial Neural Network (ANN) Approximated Function Using Numerical and Analytical Method
This thesis is to investigate the uncertainty analysis using numerical sequential perturbation method and analytical Newton approximation method. The objective of this project to propose the a new technique using numerical sequential perturbation in calculating uncertainty propagation compare to the use of analytical Newton approximation method in application where the unknown function is approximated using artificial neural network ANN. The process to determine uncertainty have five step including begin from selected function, randomize the data, function approximation and applied the numerical method in ANN and lastly determine percent of error between numerical with ANN and compare with the analytical method. The ANN was applied in MATLAB software. From the uncertainty analysis, was define that three major figure the end of this project. First figure shown the average error between numerical and analytical method without ANN are 0.03%. Second figure average error of function approximate the mass flow rate compare the actual value is 0.03%. The application with numerical method with ANN gives small uncertainty propagation error compare with analytical method where the error is 1.2% is the last graph of this project. The new technique will be approving to determine the uncertainty analysis using artificial neural network (ANN). This technique also can be applied for application in laboratory or industrial field.
Uncertainty Analysis of Artificial Neural Network (ANN) Aproximated Function for Experimental Data Using Sequential Perturbation Method
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.