A functions localized neural network with branch gates.
In this paper, a functions localized network with branch gates (FLN-bg) is studied, which consists of a basic network and a branch gate network. The branch gate network is used to determine which intermediate nodes of the basic network should be connected to the output node with a gate coefficient ranging from 0 to 1. This determination will adjust the outputs of the intermediate nodes of the basic network depending on the values of the inputs of the network in order to realize a functions localized network. FLN-bg is applied to function approximation problems and a two-spiral problem. The simulation results show that FLN-bg exhibits better performance than conventional neural networks with comparable complexity.
Xiong
Qingyu
Q
Automation College, Chongqing University, Chongqing, People's Republic of China. xiong03@hotmail.com
Hirasawa
Kotaro
K
Hu
Jinglu
J
Murata
Junichi
J
D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D005246:Feedback; D017143:Fuzzy Logic; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D013663:Teaching