Abstract
How Artificial Neural Networks (ANN) can be used to solve problems in algebra and geometry by modelling specific subnetwork nodes and connections is considered. This approach has the benefit of producing ANNs with well-defined hidden units and reduces the search to parameters which satisfy known model constraints—yet still gains from the benefits inherent in neural computing architectures.
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