Efficient algorithm for the design of multilayer feedforward neural networks

A novel design technique that builds a multilayer feedforward net for an arbitrary set of binary associations is presented. The key to the technique is to decompose the arbitrary mapping into a sequence of operations composed of three primitive operations. As each of the primitive operations is easily realizable in a simple layered feedforward net, the mapping can be realized by cascading the simple feedforward nets corresponding to the decomposition. The time spent in the construction of the net is proportional to k×n ², compared to k×2ⁿ typical of the conventional direct approaches. With such a reduction, the technique can be used in building multilayer feedforward nets for binary association problems of extremely large size