An Ensemble Network Learning Algorithm with Active Guidance for Divergent Learning Among Individual Networks

Ensemble learning algorithms enhance overall performance by combining multiple individual learners, where the diversity among individual networks is crucial for ensemble effectiveness. This paper introduces a novel ensemble network learning algorithm that actively guides individual networks toward divergent learning patterns. In implementation, this typically involves creating multiple neural network instances with shared base architectures but customized loss functions.

The algorithm's core innovation lies in theoretically decomposing the ensemble error formula, revealing the inter-network correlation factor traditionally overlooked by conventional methods. By incorporating correlation metrics into the training objective functions of individual networks, the algorithm actively guides each network to develop complementary characteristics during training. This collaborative training mechanism differs from simple parallel training by consciously cultivating diversity among networks. From a coding perspective, this requires implementing a modified loss function that combines traditional error minimization with correlation penalties, often using covariance matrices or mutual information measures.

In practical applications for transformer fault diagnosis, this method demonstrates significant advantages. Compared to the traditional three-ratio method, it captures more complex fault characteristics through its multi-network feature extraction capability. When compared to single BP neural networks, the ensemble architecture provides superior generalization performance. Notably, versus classical Bagging and Boosting methods, this algorithm exhibits more stable performance, benefiting from its active divergence guidance design philosophy. Algorithm implementation would typically involve parallel training loops with regularization terms that penalize similar decision boundaries across networks.

This divergence learning mechanism offers new perspectives for ensemble algorithm design, particularly in industrial diagnostics requiring high reliability. Through theoretically-guided collaborative training, the algorithm more effectively utilizes the complementary advantages of each network, avoiding common "groupthink" problems in ensemble learning. The code structure generally includes a correlation monitoring module that adjusts training directions dynamically based on real-time diversity measurements.