Updating optical pseudoinverse associative memories
Brian A. Telfer, David Casasent · Applied Optics · 1989
Selected algorithms for adding to and deleting from optical pseudoinverse associative memories are presented and compared. New realizations of pseudoinverse updating methods using vector inner product matrix bordering and reduced-dimensionality Karhunen-Loeve approximations (which have been used for updating optical filters) are described in the context of associative memories. Greville's theorem is reviewed and compared with the Widrow-Hoff algorithm. Kohonen's gradient projection method is expressed in a different form suitable for optical implementation. The data matrix memory is also discussed for comparison purposes. Memory size, speed and ease of updating, and key vector requirements are the comparison criteria used.