| 摘要: |
| 针对案例推理系统中案例检索的效率和质量问题，提出一种新的案例检索策略。采
用粗糙集进行案例属性约简，完成案例库优化，并计算反映专家经验的属性权重，结合相似
度计算和人工神经网络进行不同情况下的案例检索。运用UCI数据集进行了仿真对比，将其
用于数字数据网故障诊断系统中，结果表明所提出的策略在不同数据集下均具有较高的检索
效率，更加适用于实际CBR系统。 |
| 关键词: 基于案例推理 概率神经网络 粗糙集 案例检索 故障诊断系
统 |
| DOI： |
|
| 基金项目: |
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| A Case Retrieval Strategy for CBR System Based on Rough Set |
| ,YIN Shu-hua,LIN Chu-shan |
| (Department of Graduate Student Management, Xi′an Communication Institute,Xi
′an 710106, China;Command and Communication Room, Jiuquan Satellite Launch
Centre of China,Jiuquan 732750, China;Teaching Section of Military Optical Fiber Communication, Xi′an Commun
ication Insti
tute,Xi′an 710106, China) |
| Abstract: |
| A new case retrieval strategy is proposed for casebased reaso
ning(CBR) system because of the case retrieval efficiency and quality problem.
The rough set theory is adopted to implement case attribute reduction,comple
te the case
base optimization, and compute attribute weights that reflect the expert′s exp
erience firstly, and then is combined with similarity computation and artificial
ne
ural network(ANN) to accomplish case retrieval in different situation. The UCI
data set is used to simulate and compare.Application of the retrieval strategy
in the data digital networ
k fault diagnosis system indicates that the proposed case retrieval
strategy has better performance in different data sets, and it is more fit for
practical CBR system. |
| Key words: case-based reasoning(CBR) probabilistic neural network rough set case retrieve fault diagnosis system |
