存在数据缺失时高压套管的状态监测 使用进化计算

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摘要翻译：
提出了应用粒子群优化(PSO)和遗传算法(GA)相结合的神经网络来补偿高压套管分类中的缺失数据。根据IEEEC57.104、IEC599和IEEE油浸纸(OIP)套管生产率方法，利用60966套套管的DGA数据进行分类。对粒子群算法和遗传算法在计算精度和计算效率方面进行了比较。当只有一个变量丢失时，GA和PSO模拟都能够估计丢失的数据值，平均准确率为95%。然而，粒子群算法在两个变量同时丢失的情况下，准确率迅速下降到66%，而遗传算法的准确率为84%。遗传算法估计的数据比粒子群算法估计的数据更能对衬套的状态进行分类。
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英文标题：
《Condition Monitoring of HV Bushings in the Presence of Missing Data
  Using Evolutionary Computing》
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作者：
Sizwe M. Dhlamini*, Fulufhelo V. Nelwamondo**, Tshilidzi Marwala**
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The classification is done using DGA data from 60966 bushings based on IEEEc57.104, IEC599 and IEEE production rates methods for oil impregnated paper (OIP) bushings. PSO and GA were compared in terms of accuracy and computational efficiency. Both GA and PSO simulations were able to estimate missing data values to an average 95% accuracy when only one variable was missing. However PSO rapidly deteriorated to 66% accuracy with two variables missing simultaneously, compared to 84% for GA. The data estimated using GA was found to classify the conditions of bushings than the PSO.
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PDF链接：
https://arxiv.org/pdf/0705.2516

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关键词：进化计算 数据缺失 Evolutionary Optimisation SIMULTANEOUS accuracy OIP PSO 遗传算法 数据