Author/Authors :
Ke, Hai-Feng School of Computer & Computing Science - Zhejiang University City College, China , Lu, Cheng-Bo College of Engineering, Lishui University, China , Zhang, Gao-Yan School of Computer & Computing Science - Zhejiang University City College, China , Li, Xiao-Bo College of Engineering, Lishui University, China , Mei,Ying College of Engineering, Lishui University, China , Shen, Xue-Wen 3 School of Electronics and Information - Zhejiang University of Media and Communications, China
Abstract :
An optimal weight learning machine with growth of hidden nodes and incremental learning (OWLM-GHNIL) is given by adding random hidden nodes to single hidden layer feedforward networks (SLFNs) one by one or group by group. During the growth of the networks, input weights and output weights are updated incrementally, which can implement conventional optimal weight learning machine (OWLM) efficiently. The simulation results and statistical tests also demonstrate that the OWLM-GHNIL has better generalization performance than other incremental type algorithms.
Keywords :
Neural Networks , Single-Layer , Incremental Optimal , Weight Learning Machine
