摘要
作者运用三维Ｔ．Ｋｏｈｏｎｅｎ自组织人工神经网络对我国沉积碳酸盐型锰矿中菱锰矿进行分类、识别。所选研究实例中，识别成功率达１００％．结果表明，该网络性能良好，可望成为矿物识别的有效手段。
T. Kohonen’s self-organization artificial neural network model of three-dimensional structure applied to classification and recognition of minerals was presented in this paper,and a group of samlpes, rhodochrosite from sedimentary carbonate-type manganese ores in china was collected to study. The Successful rate reached 100%. The results show that the performance of the artificial neural network approach is good, and therefore it might be referred as an effective tool for prediction of recognition of minerals.
出处
《西北地质科学》
1994年第2期42-46,共5页
Northwest Geoscience
基金
中国科学院上海分院择优支持项目
关键词
矿物
分类
神经网络
自组织模型
classification of minerals, artificial neural network,T. Kohonen’s selforganization model of three-dimensional structure
