DocumentCode
317992
Title
Recursive branching network
Author
Al-Mashouq, Khalid A.
Author_Institution
Dept. of Electr. Eng., King Saud Univ., Riyadh, Saudi Arabia
Volume
2
fYear
1997
fDate
12-15 Oct 1997
Firstpage
1341
Abstract
The capacity of a 1-layer net is limited compared to a multilayer net. However, there is no explicit rule for optimal structuring and training of a multilayer net. Thus iterative methods are usually used. Here we propose a systematic way to build and train a special multilayer network called recursive branching network. The theory behind this network is presented along with experimental work done on VOWEL data set
Keywords
learning (artificial intelligence); multilayer perceptrons; optimisation; VOWEL data set; multilayer net; neural net building; neural net training; optimal structuring; optimal training; recursive branching network; Artificial intelligence; Boolean functions; Closed-form solution; Equations; Error correction; Iterative methods; Multi-layer neural network; Neural networks; Nonhomogeneous media;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Conference_Location
Orlando, FL
ISSN
1062-922X
Print_ISBN
0-7803-4053-1
Type
conf
DOI
10.1109/ICSMC.1997.638159
Filename
638159
Link To Document