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A new approach to weighted fuzzy production rule extraction from neural networks

机译:神经网络加权模糊生产规则提取的一种新方法

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There are many advantages of artificial neural networks such as high prediction accuracy, robustness, no requirements on data distribution, but knowledge captured by neural networks is not transparent to users. This results in a major problem for users of neural network-based systems. It is significant to extract rules from neural networks. This paper proposes a new method for extracting weighted fuzzy production rules from trained neural networks by structural learning based on matrix of importance index.
机译:人工神经网络具有许多优点,如高预测精度,鲁棒性,无要求数据分布,但是神经网络捕获的知识对用户来说是不透明的。这导致基于神经网络的系统的主要问题。从神经网络中提取规则很重要。本文提出了一种基于重要性指数矩阵的结构学习来提取来自训练神经网络的加权模糊生产规则的新方法。

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