Inversion of many-to-one mappings using self-organising maps

Authors:
Anne O. Mus
Affiliations:
College of Information Systems and Technology, University of Phoenix
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Abstract

Bidirectionally trained neural networks would be very useful in many circumstances. Often, we have data available for a prediction problem, but prediction of properties for unknown or new situations is only part of the story. In many cases we know the effect we wish to achieve on the output, but what we do not know is how to modify the inputs to achieve this goal. A basic problem in this area is the inversion of many to one mappings. Our work is based on the popular backpropagation neural network to predict the GDP of developing countries. These networks are integrated with a Self-Organising Map to allow the inversion of many to one mappings.