A parallel and modular multi-sieving neural network architecture with multiple control networks

We have proposed a constructive learning method, called multi-sieving learning, for implementing automatic decomposition of learning tasks and a parallel and modular multi-sieving network architecture in our previous work (1995). In this paper we present a new parallel and modular multi-sieving neural network architecture to which multiple control networks are introduced. In this architecture the learning task for a control network is decomposed into a finite set of manageable subtasks, and each subtask is learned by an individual control sub-network. An important advantage of this architecture is that the learning tasks for control networks can be learned efficiently, and therefore automatic decomposition of complex learning tasks can be achieved easily.