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Data mining and application of ship impact spectrum acceleration based on PNN neural network
داده کاوی و کاربرد شتاب طیف تأثیر کشتی بر اساس شبکه عصبی PNN-2020 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. Keywords: Ship impact environment prediction | Probabilistic neural network | Smoothing coefficient | Optimization algorithm |
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