03182nam a2200217 a 4500001001400000003000700014005001700021008004100038020002200079040000800101100002400109245018100133246017700314260003400491300006300525502009200588504002500680520221400705650001602919650002902935vtls000045567KUKTEM20251114204500.0100505t2009    my ao  f m    000 0 eng d  aTHE0006308(Local)  aUMP0 aHilmi Asyraf Razali10aUncertainty analysis of two-shaft gas turbine parameter of artificial neural network (ANN) approximated function using sequential perturbation method /cHilmi Asyraf Bin Razali3 aUncertainty analysis of two-shaft gas turbine parameter of artificial neural network (ANN) approximated function using sequential perturbation methodh[electronic resource]  aKuantan, Pahang :bUMP,c2009  axv, 63 p. :bill. (some col.) ;c30 cm. +e1 computer disc  aProject paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2009  aBibliography : p. 553 aThis 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. 0aGas turbine 0aGas turbinexPerformance