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摘要翻译：
将高斯混合模型(GMM)和支持向量机(SVM)引入到圆柱壳的故障分类中。在20个圆柱壳上测试了所提出的方法，并将其性能与使用多层感知器(MLP)的方法进行了比较。利用从振动数据中提取的模态特征对GMM、SVM和MLP进行训练。结果表明，GMM的分类准确率为98%，支持向量机的分类准确率为94%，而MLP的分类率为88%。
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英文标题：
《Fault Classification in Cylinders Using Multilayer Perceptrons, Support
Vector Machines and Guassian Mixture Models》
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作者：
Tshilidzi Marwala, Unathi Mahola and Snehashish Chakraverty
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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：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：
Gaussian mixture models (GMM) and support vector machines (SVM) are introduced to classify faults in a population of cylindrical shells. The proposed procedures are tested on a population of 20 cylindrical shells and their performance is compared to the procedure, which uses multi-layer perceptrons (MLP). The modal properties extracted from vibration data are used to train the GMM, SVM and MLP. It is observed that the GMM produces 98%, SVM produces 94% classification accuracy while the MLP produces 88% classification rates.
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PDF链接：
https://arxiv.org/pdf/0705.0197
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