Hyperborders in the Voronoi-diagram-based neural net for pattern classification

We propose a neural network to answer a point query in gsim;ⁿ partitioned based on the Voronoi diagram. Our novel design offers the potential to reduce both the number of neurons and connection weights of previous designs, employing a cost function which enables a tradeoff between the two to suit a specific implementation. Our simplified structure requires neither delay weights nor complex neurons, while retaining the main advantage of previous designs to furnish precise values for the neurons and connection weights, as opposed to trial and error iterations or ad-hoc parameters