中文说明：
通过对集成误差公式的理论分析，提出了一种能主动引导个体网络进行差异性学习的集成网络学习算法。该方法通过对集成误差的分解，使个体网络的训练准则函数中包含个体网络误差相关度的因素，并通过协同训练，引导个体网络进行差异性学习。该方法在基于油气分析的变压器故障诊断的实验结果表明，该方法的故障诊断准确率优于传统的三比值法与BP神经网络，其性能也比经典的集成方法Bagging和Boosting方法更稳定可靠。
English Description：
A learning algorithm is proposed in this paper by analyzing the error function of neural network ensembles, in which individual neural networks are actively guided to learn diversity. By decomposing the ensemble error function, error correlation terms are included in the learning criterion function of individual networks. And all the individual networks in the ensemble are leaded to learn diversity through cooperative training. The method is applied in fault diagnosis of power transformer based on Dissolved Gas Analysis. Experiment results show that, the algorithm has higher accuracy than IEC method and BP network. And the performance is more stable than conventional ensemble method, i.e., Bagging and Boosting.