The classification of froth structures in a copper flotation plant by means of a neural net
By making use of grey level dependence matrix methods, digitized images of the froth phases in a copper flotation plant were reduced to feature vectors without losing essential information of the characteristics of the froth. Classification of features extracted by means of both spatial grey level dependence matrix (SGLDM) methods, as well as neighbouring grey level dependence matrix (NGLDM) methods was investigated. By using a learning vector quantization (LVQ) neural net it was shown that froth structures could be classified satisfactorily when either NGLDM or SGLDM methods were used. When these feature sets were combined, however, the success rate of classification improved to almost 90%. This is sufficiently accurate to enable incorporation of the neural net classifier into on-line plant control systems. © 1995.