Open AccessOn Training Of Feed Forward Neural Networks
Author(s) -
Baghdad Science Journal
Publication year - 2007
Publication title -
mağallaẗ baġdād li-l-ʿulūm
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.167
H-Index - 6
eISSN - 2411-7986
pISSN - 2078-8665
DOI - 10.21123/bsj.4.1.158-164
Subject(s) - artificial neural network , feedforward neural network , computer science , computation , function (biology) , training (meteorology) , variety (cybernetics) , feed forward , backpropagation , algorithm , artificial intelligence , engineering , control engineering , physics , evolutionary biology , meteorology , biology
In this paper we describe several different training algorithms for feed forward neural networks(FFNN). In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training. The above algorithms have a variety of different computation and thus different type of form of search direction and storage requirements, however non of the above algorithms has a global properties which suited to all problems.