An ART2-BP neural net and its application to reservoir engineering

Backpropagation feedforward neural networks have been applied to pattern recognition and classification problems. However, under certain conditions the backpropagation net classifier can produce nonintuitive, nonrobust and unreliable classification results. The backpropagation net is slower to train and is not easy to accommodate new data. To solve the difficulties mentioned above, an unsupervised/supervised type neural net, namely, ART-BP net, is proposed. The idea is to use a low vigilance parameter in ART2 net to categorize input patterns into some classes and then utilize a backpropagation net to recognize patterns in each class. Advantages of the ART2-BP neural net include (1) improvement of recognition capability, (2) training convergence enhancement, and (3) easy to add new data. Theoretical analysis along with a well testing model recognition example are given to illustrate these advantages.<>