Identification of minerals using artificial neural networks based on M■ssbauer parameters

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Identification of minerals using artificial neural networks based on M■ssbauer parameters

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- Identification of minerals using artificial neural networks based on M■ssbauer parameters
- NUCLEAR TECHNIQUES Issue 7, (2000)
- 作者机构：
  
  南京大学,南京,210093
- 作者简介：
- 基金信息：
  
  国家自然科学基金!19835050;国家教委博士点基金!98－00
- DOI：
  CLC： TP183
- Published：01 July 2000
- 稿件说明：
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[1]施海蓉,肖煜明,黄红波,吴东岷,Ali A M,李民,李世民,夏元复.基于穆斯堡尔参数的人工神经网络识别矿物的方法[J].核技术,2000(07):467-474.

Identification of minerals using artificial neural networks based on M■ssbauer parameters[J]. Nuclear techniques, 2000, (7): 467-474.
[1]施海蓉,肖煜明,黄红波,吴东岷,Ali A M,李民,李世民,夏元复.基于穆斯堡尔参数的人工神经网络识别矿物的方法[J].核技术,2000(07):467-474. DOI：

Identification of minerals using artificial neural networks based on M■ssbauer parameters[J]. Nuclear techniques, 2000, (7): 467-474. DOI：

摘要

采用改进型反向传播人工神经网络，以含铁矿物穆斯堡尔参数作为样本，通过神经网络的训练，能很好地识别矿物，从而有效地提高了对矿物的识别本领。

Abstract

Minerals have been identified using Mssbauer parameters and artificial neural networks(ANN). The reported Mssbauer parameters of minerals were used to train an ANN called the improved back-propagation network. The excellent corrective ability indicates that it is probably an alternative method in Mssbauer data processing.

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