The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform

Tao Xie - One of the best experts on this subject based on the ideXlab platform.

  • Optimizing Hidden Layer Node number of BP network to estimate fetal weight
    MIPPR 2007: Medical Imaging Parallel Processing of Images and Optimization Techniques, 2007
    Co-Authors: Yuanwen Zou, Jiangli Lin, Tianfu Wang, Tao Xie
    Abstract:

    The ultrasonic estimation of fetal weigh before delivery is of most significance for obstetrical clinic. Estimating fetal weight more accurately is crucial for prenatal care, obstetrical treatment, choosing appropriate delivery methods, monitoring fetal growth and reducing the risk of newborn complications. In this paper, we introduce a method which combines golden section and artificial neural network (ANN) to estimate the fetal weight. The golden section is employed to optimize the Hidden Layer Node number of the back propagation (BP) neural network. The method greatly improves the accuracy of fetal weight estimation, and simultaneously avoids choosing the Hidden Layer Node number with subjective experience. The estimation coincidence rate achieves 74.19%, and the mean absolute error is 185.83g.

Yuanwen Zou - One of the best experts on this subject based on the ideXlab platform.

  • Optimizing Hidden Layer Node number of BP network to estimate fetal weight
    MIPPR 2007: Medical Imaging Parallel Processing of Images and Optimization Techniques, 2007
    Co-Authors: Yuanwen Zou, Jiangli Lin, Tianfu Wang, Tao Xie
    Abstract:

    The ultrasonic estimation of fetal weigh before delivery is of most significance for obstetrical clinic. Estimating fetal weight more accurately is crucial for prenatal care, obstetrical treatment, choosing appropriate delivery methods, monitoring fetal growth and reducing the risk of newborn complications. In this paper, we introduce a method which combines golden section and artificial neural network (ANN) to estimate the fetal weight. The golden section is employed to optimize the Hidden Layer Node number of the back propagation (BP) neural network. The method greatly improves the accuracy of fetal weight estimation, and simultaneously avoids choosing the Hidden Layer Node number with subjective experience. The estimation coincidence rate achieves 74.19%, and the mean absolute error is 185.83g.

Tianfu Wang - One of the best experts on this subject based on the ideXlab platform.

  • Optimizing Hidden Layer Node number of BP network to estimate fetal weight
    MIPPR 2007: Medical Imaging Parallel Processing of Images and Optimization Techniques, 2007
    Co-Authors: Yuanwen Zou, Jiangli Lin, Tianfu Wang, Tao Xie
    Abstract:

    The ultrasonic estimation of fetal weigh before delivery is of most significance for obstetrical clinic. Estimating fetal weight more accurately is crucial for prenatal care, obstetrical treatment, choosing appropriate delivery methods, monitoring fetal growth and reducing the risk of newborn complications. In this paper, we introduce a method which combines golden section and artificial neural network (ANN) to estimate the fetal weight. The golden section is employed to optimize the Hidden Layer Node number of the back propagation (BP) neural network. The method greatly improves the accuracy of fetal weight estimation, and simultaneously avoids choosing the Hidden Layer Node number with subjective experience. The estimation coincidence rate achieves 74.19%, and the mean absolute error is 185.83g.

Jiangli Lin - One of the best experts on this subject based on the ideXlab platform.

  • Optimizing Hidden Layer Node number of BP network to estimate fetal weight
    MIPPR 2007: Medical Imaging Parallel Processing of Images and Optimization Techniques, 2007
    Co-Authors: Yuanwen Zou, Jiangli Lin, Tianfu Wang, Tao Xie
    Abstract:

    The ultrasonic estimation of fetal weigh before delivery is of most significance for obstetrical clinic. Estimating fetal weight more accurately is crucial for prenatal care, obstetrical treatment, choosing appropriate delivery methods, monitoring fetal growth and reducing the risk of newborn complications. In this paper, we introduce a method which combines golden section and artificial neural network (ANN) to estimate the fetal weight. The golden section is employed to optimize the Hidden Layer Node number of the back propagation (BP) neural network. The method greatly improves the accuracy of fetal weight estimation, and simultaneously avoids choosing the Hidden Layer Node number with subjective experience. The estimation coincidence rate achieves 74.19%, and the mean absolute error is 185.83g.

M S Gelder - One of the best experts on this subject based on the ideXlab platform.

  • fuzzy logic adapted nodal training parameter
    Proceedings of International Conference on Neural Networks (ICNN'96), 1996
    Co-Authors: M S Gelder
    Abstract:

    A technique is outlined for improving the learning rate of a multiLayer perceptron (MLP) network. Each network Node is assigned its own training rate parameter which is adapted using fuzzy logic as part of the error backpropagation process. This involves the development of target values for Hidden Layer Node output. These values are based on the current network weight state and are therefore different for each epoch. Using two test vector distributions it is demonstrated that this approach can reduce MLP convergence time and is compared to three other training methods: standard backpropagation, fuzzy adapted global training rate parameter, and the delta-bar-delta learning rule.