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BP neural network-based web service selection algorithm in the smart distribution grid

机译:智能配电网中基于BP神经网络的Web服务选择算法

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摘要

A good web selection algorithm can provide the most suitable service for users. However, known for its slow convergence rate and proneness of oscillation in its learning process, the traditional error back propagation neural network algorithm cannot be applied in the service selection scenarios of actual smart distribution grid. In order to meet the requirements of telecommunication technology for smart distribution grid and improve the quality of telecommunication service, this paper proposes an improved error back propagation algorithm, in which the learning factor can be self-adjusted with every iteration. The simulation results show an optimization of the training speed and an oscillation reduction in the learning process with the new algorithm, thus obvious optimizing the web services selection in smart distribution grid.
机译:一个好的网页选择算法可以为用户提供最合适的服务。然而,传统的误差反向传播神经网络算法以其慢的收敛速度和学习过程中的振荡倾向而著称,因此无法应用于实际智能配电网的服务选择场景中。为了满足电信技术对智能配电网的需求,提高电信服务质量,提出了一种改进的误差反向传播算法,该算法可以在每次迭代中自动调整学习因子。仿真结果表明,采用新算法可以优化训练速度,减少学习过程中的振荡,从而明显优化了智能配电网中的Web服务选择。

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