A backpropagation system for hypercubes

A backpropagation system for a hypercube which will select one of two implementations, depending on the size of the application, is described. One algorithm executes quickly at the cost of storage. The other optimizes storage at the cost of execution. Both algorithms have considerable message passing. The system is menu driven and has a set of tools to allow the user to determine the correct initial weight matrix W more accurately when the standard guess does not work. This set of tools is especially appropriate for an interactive supercomputer such as an Intel Hypercube. Although the system has been designed to work for a wide range of applications, the authors are especially interested in using it with very large artificial neural systems to do protein identification and classification