One-to-many mappings represented on feed-forward networks.

2 University of Alberta Multiplayer perceptrons or feed-forward networks are generally trained to represent functions or many-to-one (m-o) mappings. This creates a problem if the training data exhibits the property of many-to-many or almost many-many valued-ness because the model, which generated the data, was many-to-many. Therefore in this paper a modified feed-forward network and training algorithm is considered to represent a multi-valued mappings. The solution consists of adding another input to the standard feed-forward network and of modifying the training algorithm. This additional input will generally have no training values provided and an amended training algorithm is used to find its values. The modified feed-forward network and training method has been successfully applied both in representing the mapping implied by data generated by multivalued functions and in representing the mapping implied by data obtained from benchmark databases.