Negative Correlation Learning Based on Neural network
Yi Ding · Jisuanji fangzhen · 2007
Artificial Neural network ensemble (ANNE) is a hot topic of neural computing, which has been maturely applied in many fields. A neural network ensemble is a very successful technique where the outputs of a set of separately trained neural network are combined to form an unified prediction. Negative correlation learning (NCL) algorithm for training ANNE is introduced in this paper to encourage different individual networks in an ensemble to learn different parts or aspects of a training data so that the ensemble can learn the whole training data better. NCL in this paper can create negatively correlated neural networks using a correlation penalty term in the error function. It can also combine the advantages of original negative correlation learning and BP algorithm with impulse, thus making the developed algorithm into a batch algorithm with competed extending ability and fast learning speed.