Uncertainty Analysis Of Artificial Neural Network Ann Aproximated Function For Experimental Data Using Sequential Perturbation Method
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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.
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 Analysis of Two-shaft Gas Turbine Parameter of Artificial Neural Network (ANN) Approximated Function Using Sequential Perturbation Method
This thesis deals with the finding of uncertainty for two-shaft gas turbine involving its parameter where Artificial Neural Network (ANN) approximated function in association with sequential perturbation method will be applied. Previously, in order for operators to increase the efficiency of two-shaft gas turbine, experimental method was done where each variable input related with the output which is the thrust produced, Fn need to be change from time to time in order to attain the most possible outcome. Moreover, alot of expensive jigs required to perform this experiment as every parameter involved will be measured with their respective equipments hence as the parameter involved increases, the cost to operate the experiment will also increases. The approach in analysing uncertainty of two-shaft gas turbine parameter is multivariable nonlinear complex function with five inputs and output were randomly generated and their function was approximated via ANN using feed-forward and backpropagation network. Uncertainty outcome through sequential perturbation with ANN will then be compare with the uncertainty outcome using sequential perturbation analytically. Lastly, percentage error between both methods shall be compute so as to prove that uncertainty analysis using sequential perturbation with ANN can also be use rather than by any other method. Average percentage error between Newton approximation (analytical method) and sequential perturbation (numerical method) retrieved is 0.001%. Meanwhile, the average percentage error between actual thrust produced and approximated thrust produced possessed is 0.213%. These values mentioned is not the vital part of this study as their intention was to substantiate whether ANN approximated function can be apply in order to proceed with the crucial part of all which is the average percentage error between uncertainty value via sequential perturbation with ANN and Newton approximation analytically where the value acquired is 0.476%. From these results, it is proven that only a set of data with input and output is necessary for the sake of predicting the output's uncertainty, UFn hence intensifies the efficiency of two-shaft gas turbine.