NEARLY OPTIMAL CORRELATIONS FOR SHIFT-INVARIANT ASSOCIATIVE MEMORIES

The optical implementation of the Hopfield algorithm in shift-invarian t holographic associative memories is based on the use of correlators with matched filters. However, it is well known that such correlators have poor discrimination. We propose nearly optimal correlation design s for associative memories based on correlation filters that have maxi mum discrimination ability. These new designs avoid large cross-correl ation-peak terms caused by a mismatch between partial input and the fu lly stored information in the filter. These solutions rely on whitened spectra of the stored and the recalled information. Computer simulati ons are made of eight different combinations.