A signal processing perspective to the operational characteristics of perceptron and Hopfield associative memory neural networks

Projection method formulations of the perceptron and Hopfield associative content-addressable memory (ACAM) neural nets are presented. It is shown that the well-known single-layer-perceptron learning algorithm can be formulated using the method of projections onto convex sets (POCS) and that its performance can be improved using this method. The operation of a modified, binary-valued Hopfield ACAM is shown to be equivalent to the method of generalized projections. A direct extension of the binary-valued ACAM to the continuous-valued case lends itself to a POCS formulation