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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.