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基于GA-SVM回归的成矿有利度预测方法探讨/Discussion on Prediction Method for the Contribution Degrees to the Metallogenesis Based on GA-SVM Regression[J]
Authors
Li Dong
Sun Weidong
+11 more
Wang Jinlin
Wu Yanshuang
Zhou Kefa
中国科学院大学,北京 100049
中国科学院新疆生态与地理研究所,新疆矿产资源研究中心,新疆 乌鲁木齐 830011
吴艳爽
周可法
孙卫东
新疆维吾尔自治区地质矿产勘查开发局信息中心,新疆 乌鲁木齐,830000
李东
王金林
Publication date
1 January 2014
Publisher
Abstract
将改进的非线性技术(GA-SVM)应用于成矿预测,为成矿有利度预测方法提供一种新思路。在分析哈图矿集区成矿有利度基础上,选取28个学习样本、10个与成矿有关的地质变量,应用基于遗传算法(GA)寻优的支持向量机(SVM)方法,对成矿有利度进行建模,并与BP神经网络模型预测结果进行比较。结果表明,GA-SVM回归预测模型能很好地拟合成矿有利度与各地质变量间的非线性关系。样本数量有限时,GA-SVM比BP神经网络具较高的拟合精度,更适合非线性成矿预测工作,具较强的推广意义
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Last time updated on 29/11/2016