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首页> 外文会议>International conference on teaching and computational science;WTCS 2009 >Applying Case-Based Reasoning for Mineral Resources Prediction
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Applying Case-Based Reasoning for Mineral Resources Prediction

机译：基于案例的推理在矿产资源预测中的应用

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摘要

Case-Based Reasoning (CBR), a well known Artificial Intelligence (AI) technique, which consists of retrieving, reusing, revising, and retaining cases, has already proven its effectiveness in numerous industries. In this research, we try to adopt CBR technique in mineral resources prediction. A model for mineral resources prediction is proposed in this paper, which can support the processes of case-based reasoning in mineral resources prediction such as case representation, indexing, retrieving and case revising. It mainly includes Feature tree and FSM algorithm and it is different from traditional model. At last, an experiment of iron resources prediction is performed in Eastern Kunlun Mountains, China. The results indicated that the model proposed in this paper is suitable for regional metallogenic prediction.
机译：基于案例的推理（CBR）是一种众所周知的人工智能（AI）技术，由检索，重用，修改和保留案例组成，已经在众多行业中证明了其有效性。在这项研究中，我们尝试在矿产资源预测中采用CBR技术。本文提出了一种矿产资源预测模型，该模型可以支持矿产资源预测中基于案例的推理过程，如案例表示，索引编制，检索和案例修改。它主要包括特征树和FSM算法，与传统模型不同。最后，在中国东部昆仑山进行了铁资源预测实验。结果表明，本文提出的模型适用于区域成矿预测。

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