{"title":"Water mine data fusion and model recognition","authors":"Haibo Liu, Guochang Gu, Jing Shen, Yan Fu","doi":"10.1109/ICIA.2005.1635152","DOIUrl":null,"url":null,"abstract":"It is significant for a MCS (mine countermeasure system) to recognize the model of a water mine exactly in order to take right destroying measures. In this paper, the ABNET proposed by L.N. de Castro is simplified and employed in a multi-agent-based MCS for fusing the feature data and recognizing the model of water mines. The simplified ABNET (sABNET) is a two-layer Boolean network which number of outputs is adaptable according to the task and which recognition precision can be controlled by the immune affinity threshold. Compared with Castro's work, the sABNET converges more quickly.","PeriodicalId":136611,"journal":{"name":"2005 IEEE International Conference on Information Acquisition","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE International Conference on Information Acquisition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIA.2005.1635152","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
It is significant for a MCS (mine countermeasure system) to recognize the model of a water mine exactly in order to take right destroying measures. In this paper, the ABNET proposed by L.N. de Castro is simplified and employed in a multi-agent-based MCS for fusing the feature data and recognizing the model of water mines. The simplified ABNET (sABNET) is a two-layer Boolean network which number of outputs is adaptable according to the task and which recognition precision can be controlled by the immune affinity threshold. Compared with Castro's work, the sABNET converges more quickly.
水雷对抗系统准确识别水雷模型，对采取正确的破坏措施具有重要意义。本文对L.N. de Castro提出的ABNET进行了简化，并将其应用于基于多智能体的MCS中，用于水矿特征数据融合和模型识别。简化的ABNET (sABNET)是一个两层布尔网络，它的输出数量可以根据任务的不同而变化，识别精度可以通过免疫亲和阈值来控制。与卡斯特罗的工作相比，sABNET的收敛速度更快。
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