Title :
Recursive branching network
Author :
Al-Mashouq, Khalid A.
Author_Institution :
Dept. of Electr. Eng., King Saud Univ., Riyadh, Saudi Arabia
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;
Conference_Titel :
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-4053-1
DOI :
10.1109/ICSMC.1997.638159
