A new approach to the extraction of ANN rules and to their generalization capacity through GP
A new approach to the extraction of ANN rules and to their generalization capacity through GP
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DOI：
10.1162/089976604323057461
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发表时间：
2004-07-01
影响因子：
2.9
通讯作者：
Rivero, D
中科院分区：
文献类型：
--
作者：
Rabuñal, JR;Dorado, J;Rivero, D
人工神经网络规则的提取已经使用了各种各样的技术，但大多数技术都集中在特定类型的网络及其训练上。很少有方法将ANN规则提取作为独立于其体系结构、训练和权重、连接和激活函数的内部分布的系统来处理。本文提出了一种基于遗传规划的人工神经网络规则提取方法，无论其结构如何。该策略基于先前的算法，旨在通过人类可以理解的符号规则来实现人工神经网络所具有的泛化能力。
Various techniques for the extraction of ANN rules have been used, but most of them have focused on certain types of networks and their training. There are very few methods that deal with ANN rule extraction as systems that are independent of their architecture, training, and internal distribution of weights, connections, and activation functions. This article proposes a methodology for the extraction of ANN rules, regardless of their architecture, and based on genetic programming. The strategy is based on the previous algorithm and aims at achieving the generalization capacity that is characteristic of ANNs by means of symbolic rules that are understandable to human beings.