将语义融入ART

A. Guazzelli, B. de Faria Leao
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引用次数: 4

摘要

本文将模糊ARTMAP与组合神经模型- cnm神经网络进行比较,以解决医学诊断问题。这两种不同的神经网络模型在hycone中实现,hycone是一个紧密耦合的混合连接专家系统,将框架与神经网络集成在一起。hycone的第一个原型使用了CNM模型,并被验证用于先天性心脏病的诊断。为了评估hycone与其他知名神经网络架构的性能,将hycone的CNM网络替换为模糊ARTMAP网络。第二个原型提交到与第一个原型评估中使用的相同的验证协议。给出了CNM和模糊ARTMAP的比较结果,并对模糊ARTMAP的改进提出了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incorporating semantics to ART
This paper describes a comparison between fuzzy ARTMAP and combinatorial neural model-CNM neural networks to solve diagnostic problems in medicine. These two different neural networks models were implemented in HYCONES, a tightly coupled hybrid connectionist expert system that integrates frames with neural networks. HYCONES first prototype used the CNM model and was validated for congenital heart diseases diagnoses. In order to assess HYCONES performance with other well-known neural network architectures, HYCONES' CNM networks were replaced by fuzzy ARTMAP networks. This second prototype was submitted to the same validation protocol used in the assessment of the first one. The results of the comparison between CNM and fuzzy ARTMAP and a proposal to modify fuzzy ARTMAP are presented and discussed.<>
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