四层参数自调整BP神经网络模型及其在人口死亡率预测中的应用
Four-Layer Parameter Self-Adjusting BP Neural Network Model and Its Application in Population Mortality Prediction
为有效提高神经网络的学习效率并降低其陷入局部极小值的概率, 文章构建了四层参数自调整BP神经网络模型, 该模型具有四层特殊的网络结构, 采用附加动量法与自适应法调整参数. 数值试验表明, 与常规的BP神经网络相比, 该方法的学习收敛速度与预测精度均有较大改进. 鉴于人口死亡率的复杂变化趋势, 使用单个模型无法准确预测, 文章同时建立了GM
In order to effectively improve the learning efficiency of the neural network and reduce its probability of being trapped in the local minimum, a four-layer parameter self-adjusting BP neural network model was constructed in this paper. The model had four special network structures, used additional momentum method and adaptive method to adjust parameters. Numerical experiments showed that compared with the conventional BP neural network, the learning convergence speed and prediction accuracy of this method were greatly improved. Due to the complex changes in the mortality rate of the population, using a single model can not be accurately predicted. This paper also established the GM
BP神经网络 / 模型平均 / 人口死亡率 / 预测. {{custom_keyword}} /
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