Prestructuring ANNs via a priori knowledge (Abstract only)

The work to be described has as its objective development of a constructive method that uses certain a priori information about a problem domain to design the starting structure of an artificial neural network (ANN). For the prestructuring process, there is motivation to move away from homogeneous structures to ones that comprise modules of smaller ANNs. Issues of concern include physical realizability, scalability of training time with large numbers of connections, and successful generalization. The method explored is based on a general systems theory methodology (here called GSM) that calculates a kinds of structural information of the problem domain via analyzing I/O pairs from that domain. This GSM-based information is used for developing a modularized ANN starting structure. Extensive experiments on 3-input, 1-output Boolean mappings verify our predictions. In addition, the experiments indicate that the GSM-based modularized-ANN design is `conservative' in the sense that the PS of the modularized ANN contains at least all the mappings included in the GSM category used to design the ANN. Experiments with 5-input, 1-output Boolean functions provide further support of the conclusions.