@article {IOPORT.06075849,
    author       = {Yu, Xin and Chen, Qingfeng},
    title        = {Convergence of gradient method with penalty for Ridge Polynomial neural network.},
    year         = {2012},
    journal      = {Neurocomputing},
    volume       = {97},
    issn         = {0925-2312},
    pages        = {405-409},
    publisher    = {Elsevier Science Publishers B.V., Amsterdam},
    doi          = {10.1016/j.neucom.2012.05.022},
    abstract     = {Summary: In this paper, a penalty term is added to the conventional error function to improve the generalization of the Ridge Polynomial neural network. In order to choose appropriate learning parameters, we propose a monotonicity theorem and two convergence theorems including a weak convergence and a strong convergence for the synchronous gradient method with penalty for the neural network. The experimental results of the function approximation problem illustrate the above theoretical results are valid.},
    identifier   = {06075849},
}