02576nam a2200241 a 4500 vtls000047106 KUKTEM 20251114204433.0 100713t2009 my dao f m 000 0 eng d THE0005494(Local) 201905160939 hanafiah 201107132334 VLOAD 201012100812 ida 201007131455 Fida UMP QA76.87 .K36 2009 rs. Bc Kamal Ariffin Mohamad Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method / Kamal Ariffin Mohamad Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method [electronic resource] Kuantan, Pahang : UMP, 2009 xv, 79 p. : ill. (some col.) ; 30 cm. + 1 computer disc Project paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2009 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. Neural networks (Computer science) 0 0 0 1 20000 20000 2019-09-04 0 QA76.87 .K36 2009 rs. Bc 0000046255 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 20000 20000 2019-09-04 0 CD 4428 | QA76.87 .K36 2009 rs. Bc 0000046256 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 2012 2018 VTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*9992