CmI奇宇称光谱能级的模式识别研究

应用新的模式识别方法ＰＣＡ－ＢＰＮ（ＰｒｉｎｃｉｐａｌＣｏｍｐｏｎｅｎｔＡｎａｌｙｓｉｓ－ＢａｃｋＰｒｏｐａｇａｔｉｏｎＮｅｔｗｏｒｋ）指认ＣｍＩ奇宇称未知能级，支持了前人应用传统的ＫＮＮ（ＫＮｅａｒｅｓｔＮｅｉｇｈｂｏｒｓ）等模式识别方法及对传神经网络方法（ＣｏｕｎｔｅｒＰｒｏｐａｇａｔｉｏｎｎｅｔｗｏｒｋ，ＣＰＮ）对大部分谱线的指认，进一步确认了这些组态的归属，鉴别了ＫＮＮ等与ＣＰＮ不同的预报

Study on the Curium I Odd-Parity Energy Levels Using Pattern Recognition Techniques

A new pattern recognition technique PCA-BPN(principal component analysis-back propagation network) has been used to assign the unknown electronic configurations of odd-parity energy levels of the first spectrum of curium (Cm I ). The obtained results show that (1) most previous predictions given by KNN(K nearest neighbours) and CPN(counter propagation network) are further confirmed;(2) several energy levels, which could not be clearly assigned by KNN etc., are predicted to be in good agreement with the assignments of the CPN;(3) two energy levels which were wrongly predicted by the CPN are now corrected using the PCA-BPN and the new assignments are supported by the traditional pattern recognition technique, PCA-NLM(principal component analysis nonlinear mapping).