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首页> 外文会议>IEEE International Symposium on Asynchronous Circuits and Systems >A Survey on Intelligent Information Processing System: A Machine Ailment Diagnosing Based on KNN Similarity Degree
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A Survey on Intelligent Information Processing System: A Machine Ailment Diagnosing Based on KNN Similarity Degree

机译：智能信息处理系统综述：基于KNN相似度的机器故障诊断

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摘要

Intelligent Information Processing System has successful application in informationization of traditional industry. Exact addressing the stock case's ailment type and roots as quickly as possible has been the weight of developing information technology for veterinary. In order to assist human veterinarian expert diagnose animal ailment, this work proposes a machine diagnosing model based on KNN ailment-similarity-degree pattern recognition. The project crew devises 3 similarity distance measuring methods including Lee distance and Jaro distance, which are addressed to the uncertainty factor vector pattern and fuzzy membership pattern. In addition, the software architecture of the machine diagnosing model and diagnosing algorithm is constructed in detail. Field experimental statistics demonstrate that compared with the individual human veterinary expert, the proposed model achieve a preferable accuracy rate of diagnosis over 80%, and low a rate of misdiagnosis obviously, which is an alternate of existent ones with great potential.
机译：智能信息处理系统已成功应用于传统行业的信息化。尽快解决畜禽疾病的类型和根源一直是发展兽医信息技术的重中之重。为了帮助人类兽医专家诊断动物疾病，本文提出了一种基于KNN疾病相似度模式识别的机器诊断模型。项目团队设计了3种相似性距离测量方法，包括Lee距离和Jaro距离，分别针对不确定性因子矢量模式和模糊隶属度模式。另外，详细构建了机器诊断模型和诊断算法的软件架构。现场实验统计表明，与个体兽医专家相比，该模型的诊断准确率达到80％以上，误诊率明显降低，是已有潜力的替代方案。

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