Date of Award
2002
Document Type
Thesis
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Pandya, Abhijit S.
Abstract
Backpropagation is a standard algorithm that is widely employed in many neural networks. Due to its wide acceptance and implementation, a standard benchmark for evaluating the performance of the algorithm is a handy tool for software design and development. The object of this thesis is to propose the use of the classic XOR problem for the performance evaluation of the backpropagation algorithm, with some variations on the input data sets. This thesis covers background work in this area and discusses the results obtained by other researchers. A series of test cases are then developed and run to perform the performance analysis of the backpropagation algorithm. As the performance of the networks depends strongly on the inputs, the effect of variation of the design parameters for the networks are evaluated and discussed.
Extent
109 p.
Recommended Citation
Malladi, Sasikanth., "Performance analysis of back propagation algorithm using artificial neural networks" (2002). Electronic Theses and Dissertations 2000 - 2009. 437.
https://digitalcommons.fau.edu/etd_general_5/437
Identifier
FADT12961
Comments
College of Engineering and Computer Science|FAU Electronic Theses and Dissertations Collection|Thesis (M.S.)--Florida Atlantic University, 2002.|College of Engineering and Computer Science