A divide-and-conquer methodology for modular supervised neural network design

A novel learning strategy based on the divide-and-conquer concept is proposed to effectively overcome the slow learning speed and hard-determined network size problems in supervised learning neural networks. The proposed method first partitions the whole complex training set into several manageable subsets and then generates small size networks to `conquer´ (or learn) all these training subsets. In order to achieve efficient partition on a train set, we have proposed an error correlation partitioning (ECP) scheme such that sub-training-sets are formed with small (acceptable) training error. Based on this learning strategy, a self-growing modular neural network system can be developed. By applying the proposed learning strategy, a neural network is not only useful for pattern classification problems but also for continuous valued function approximation problems