On the variance reduction of neural networks-experimental results for an automotive application

We show the results of an empirical comparison using neural network learning methods which reduce the estimation variance by combining the outputs of individual networks. These network topologies are also known as committee or ensemble of networks. Alternatively, we examine constructive networks which adapt their internal complexity, reducing the overfitting problem automatically. Both classes of networks have been compared within the framework of an engine knock detection system taking into account the generalization performance, the network size and the needed computational load for the training procedure