A parallel and modular multi-sieving neural network architecture for constructive learning

Presents a parallel and modular multi-sieving neural network (PMSN) architecture for constructive learning. This PMSN architecture is different from existing constructive learning networks such as the cascade correlation architecture. The constructing element of the PMSNs is a compound modular network rather than a hidden unit. This compound modular network is called a sieving module (SM). In the PMSN, a complex learning task is decomposed into a set of relatively simple subtasks automatically. Each of these subtasks is solved by a corresponding individual SM and all of these SMs are processed in parallel.