Classification of valence changes of trivalent rare earth ions in alkalineearth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE 2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were pe rformed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant an alysis and stepwise discriminant analysis were adopted. A learning set cons isting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rat es from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were o btained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserv ed.