Convergence of recursive associative memories obtained using the multilayered perceptron

The authors describe two types of associative memory which are obtained by iterating a multilayered perceptron recursively after training it to auto-associate. The convergence of the associative memories with real-valued output and additional weights connecting the input and output layers directly is demonstrated analytically. Computational results are presented to illustrate the key concepts used in the proof, and also to characterise the capacity of these networks, comparing them with the Hopfield model and an alternative formulation of the multilayered auto-associative memory with thresholding to binary outputs. The capacity of the real-valued memory exceeds that of the alternative formulations when training with noise is used.