Neural networks: Studying their decision-making rules

Anatolii Petrenko, Ilya Vokhranov
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引用次数: 0

Abstract

The question of a better understanding of the behavior of neural networks is quite relevant, especially in industries with a high level of risks. To solve this problem, the possibilities of the new DeepRED decomposition algorithm, capable of extracting decision-making rules by deep neural networks with several hidden layers, are explored in the paper. The study of the DeepRED algorithm was carried out on the example of extracting the rules of an experimental neural network during the classification of images of the MNIST database of handwritten digits, which made it possible to reveal a number of limitations of the DeepRED algorithm.
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神经网络:研究其决策规则
更好地理解神经网络的行为是一个非常重要的问题,特别是在高风险的行业。为了解决这一问题,本文探讨了新的DeepRED分解算法的可能性,该算法能够通过具有多个隐藏层的深度神经网络提取决策规则。以MNIST手写体数字数据库图像分类过程中提取实验神经网络规则为例,对DeepRED算法进行了研究,揭示了DeepRED算法的一些局限性。
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来源期刊
Sistemni Doslidzena ta Informacijni Tehnologii
Sistemni Doslidzena ta Informacijni Tehnologii Computer Science-Computational Theory and Mathematics
CiteScore
0.60
自引率
0.00%
发文量
22
审稿时长
52 weeks
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