title: A functions localized neural network with branch gates.
abstract: 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.
authors: Automation College, Chongqing University, Chongqing, People's Republic of China. xiong03@hotmail.com
authors: Qingyu
authors: Q
authors: Xiong
authors: Kotaro
authors: K
authors: Hirasawa
authors: Jinglu
authors: J
authors: Hu
authors: Junichi
authors: J
authors: Murata
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D005246:Feedback; D017143:Fuzzy Logic; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D013663:Teaching