An improved Voronoi-diagram-based neural net for pattern classification

We propose a novel two-layer neural network to answer a point query in R/sup n/ which is partitioned into polyhedral regions; such a task solves among others nearest neighbor clustering. As in previous approaches to the problem, our design is based on the use of Voronoi diagrams. However, our approach results in substantial reduction of the number of neurons, completely eliminating the second layer, at the price of requiring only two additional clock steps. In addition, the design process is also simplified while retaining the main advantage of the approach, namely its ability to furnish precise values for the number of neurons and the connection weights necessitating neither trial and error type iterations nor ad hoc parameters.