Abstract The selection of the smoothing coefficient of the probabilistic neural network directly affects the performance of the network. Traditionally, all the mode layer neurons use a uniform smoothing coefficient, and then the optimal smoothing parameters suitable for this problem are searched by the optimization algorithm. In this study, the smoothing coefficients of the mode layer neurons connected by the same summation layer are set to the same value, which not only reflects the relationship between the training samples of the same pattern, but also highlights the difference between the training samples of different modes. Two probabilistic neural network models are applied to the ship impact environment prediction respectively. The results show that the classification effect of multiple smoothing factors is further improved than the single smoothing factor network.

中文翻译：

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基于PNN神经网络的船舶碰撞谱加速数据挖掘与应用

摘要 概率神经网络平滑系数的选择直接影响网络的性能。传统上，所有模式层神经元都使用统一的平滑系数，然后通过优化算法搜索适合该问题的最优平滑参数。本研究将同一求和层连接的模式层神经元的平滑系数设置为相同的值，既反映了同一模式的训练样本之间的关系，也突出了不同模式训练样本之间的差异。不同的模式。两种概率神经网络模型分别应用于船舶撞击环境预测。