Noise Supplement Learning Algorithm for Associative Memories Using Multilayer Perceptrons and Sparsely Interconnected Neural Networks

摘要

At present, we have proposed associative memories using multilayer perceptrons (MLPs) and sparsely interconnected neural networks (SINNs), named MLP-SINN, to improve SINNs without increasing their interconnections. MLP-SINN is more suitable for hardware implementation than SINN with a large number of interconnections. However, the capabilities of MLP and SINN are not effectively used in the conventional MLP-SINN, because they are synthesized independently. In this paper, we propose the noise supplement learning algorithm to improve MLP-SINN associative memories.