Document Type
Thesis
Date of Award
1-31-1991
Degree Name
Master of Science in Electrical Engineering - (M.S.)
Department
Electrical Engineering
First Advisor
Constantine N. Manikopoulos
Second Advisor
Irving Y. Wang
Third Advisor
George Antoniou
Abstract
This thesis describes the Neural Network approach to design predictor using Delta and Generalized Delta Rule. The predictor is designed by supervised training based on the typical sequence of pixel values. Neural Network is used to find the coefficients of the predictor. Both 1-D and 2-D scheme of the pixels as well as linear and non-linear correlations are used to find the coefficients by training. Different combinations of pixels are used to find the "best" combination among the order of the predictor.
Recommended Citation
Shah, Prashant M., "Comparative study of prediction gain based on neural network architecture" (1991). Theses. 2611.
https://digitalcommons.njit.edu/theses/2611
