一种有助于知识获取的神经模糊遗传算法系统架构

Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174) Pub Date : 1998-03-21 DOI:10.1109/IJSIS.1998.685417

L. Brasil, F. de Azevedo, J. Barreto, M. Noirhomme-Fraiture

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引用次数: 2

摘要

知识获取过程主要是抽取领域专家的知识。这项工作的目的是尽量减少知识获取过程的内在困难。为了实现这一目的，在短时间间隔内获得领域专家提供的所有可能规则和一组示例。所提出的混合专家系统采用了一种新的方法，最大限度地减少了知识获取的困难。为了构建这种混合架构，使用了几种工具:符号范式、连接主义范式、模糊逻辑和遗传算法。

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A neuro-fuzzy-GA system architecture for helping the knowledge acquisition process

The knowledge acquisition process consists on extracting knowledge of a domain expert. This work aims to minimize the intrinsic difficulties of the knowledge acquisition process. For achieve this purpose, all possible rules from the domain expert and a set of example were obtained for a short time interval. The proposed hybrid expert system minimizes the knowledge acquisition difficulties using a new methodology. To build this hybrid architecture, several tools were used: symbolic paradigm, connectionist paradigm, fuzzy logic and genetic algorithm.

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Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)

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