How to find different neural networks by negative correlation learning

Two penalty functions are introduced in the negative correlation learning for finding different neural networks in an ensemble. One is based on the average output of the ensemble. The other is based on the classification. The idea of penalty function based on the average output is to make each individual network has the different output value to that of the ensemble on the same input. In comparison, the penalty function based on the classification is to lead each individual network to have different class to that of the ensemble on the same input. Experiments on a classification task show how the negative correlation learning generates different neural networks with two different penalty functions.