Uncertainty analysis of artificial neural network (ANN) aproximated function for experimental data using sequential perturbation method

• Main Author: Mohd Jukimi, Joni
• Format: Undergraduates Project Papers
• Language: English; English; English; English; English
• Published: 2009
• Subjects: QA Mathematics
• Online Access: http://umpir.ump.edu.my/id/eprint/907/1/Mohd_Jukimi_Joni.pdf; http://umpir.ump.edu.my/id/eprint/907/4/Uncertainty%20analysis%20of%20artificial%20neural%20network%20%28ann%29%20aproximated%20function%20for%20experimental%20data%20using%20sequential%20pertubation%20method%20-%20chapter%201.pdf; http://umpir.ump.edu.my/id/eprint/907/5/Uncertainty%20analysis%20of%20artificial%20neural%20network%20%28ann%29%20aproximated%20function%20for%20experimental%20data%20using%20sequential%20pertubation%20method%20-%20references.pdf; http://umpir.ump.edu.my/id/eprint/907/6/Uncertainty%20analysis%20of%20artificial%20neural%20network%20%28ann%29%20aproximated%20function%20for%20experimental%20data%20using%20sequential%20pertubation%20method%20-%20table%20of%20content.pdf; http://umpir.ump.edu.my/id/eprint/907/7/Uncertainty%20analysis%20of%20artificial%20neural%20network%20%28ann%29%20aproximated%20function%20for%20experimental%20data%20using%20sequential%20pertubation%20method%20-%20abstract.pdf; http://umpir.ump.edu.my/id/eprint/907/

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 f...