This project will study the capabilities of a new class of associative neural memories (ANM) developed by the P.I. in prior work. These systems show promise of expanded memory capacity and acceptable convergence. In particular, one system, borrowing concepts from the Ho-Kashyap algorithm for pattern classification, has outperformed the standard Hopfield and Kohonen ANM's in preliminary tests. This project will institute a more comprehensive series of tests, including diagnostic tests which will be used to guide the design of a more powerful ANM. In developing new designs, the P.I. will pay special attention to the similarity measures used in the various algorithms, to multilayer designs, and to applications such as digital multiplication which require high network capacity.
