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基于混合推理机制的故障诊断专家系统
引用本文:何波,刘全利,王越,王华秋.基于混合推理机制的故障诊断专家系统[J].微计算机信息,2006,22(25):192-194.
作者姓名:何波  刘全利  王越  王华秋
作者单位:400050,重庆工学院计算机科学与工程学院
基金项目:重庆市科技重大项目;湖南省重点实验室基金
摘    要:多数故障诊断专家系统采用单一的推理机制,或者基于规则的推理,或者基于事例的推理。而这两种推理机制都各有优缺点,采用单一推理机制会造成诊断的不准确性。论文将基于规则的推理和基于事例的推理相结合,设计了混合推理机制。在此基础上,论文设计了一个既有专家知识库,又有故障事例库,具有自学习能力的故障诊断专家系统(AFDES)。实验结果表明,论文设计的混合推理机制是比较有效的。

关 键 词:故障诊断  专家系统  基于规则的推理  基于事例的推理
文章编号:1008-0570(2006)09-1-0192-03

Fault Diagnosis Expert System Based on Mixed Reasoning Mechanism
He Bo,Liu Quanli,Wang Yue,Wang Huaqiu.Fault Diagnosis Expert System Based on Mixed Reasoning Mechanism[J].Control & Automation,2006,22(25):192-194.
Authors:He Bo  Liu Quanli  Wang Yue  Wang Huaqiu
Abstract:Most fault diagnosis expert system adopted single reasoning mechanism, or rule-based reasoning or case-based reasoning. However, each reasoning mechanism had advantages and disadvantages. The adoption of anyone would lead to inaccurate diagnosis. The paper designed mixed reasoning mechanism based on rule-based reasoning and case-based reasoning. On the base of this, the paper designed a self-learning fault diagnosis expert system, namely, AFDES, which had expert knowledge database and fault case database. The experiments indicate that designed mixed reasoning mechanism is effective.
Keywords:fault diagnosis  expert system  rule-based reasoning  case-based reasoning
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