Further use of simple parallel processing with Asa H

The Asa H architecture consists of a hierarchical memory assembled out of clustering modules and feature detectors.  I have done some feature extraction outside of the main Asa H program using an autoassociative neural network trained on the various cases from the Asa H casebase.  The hidden layer of the autoassociator network provides the feature detectors.  These networks require considerable time to train by backpropagation.  A half dozen or so networks can be trained in parallel on individual computers.