A neural network with a single recurrent unit for associative memories based on linear optimization

Abstract

Recently, some continuous-time recurrent neural networks have been proposed for associative memories based on optimizing linear or quadratic programming problems. In this paper, a simple and efficient neural network with a single recurrent unit is proposed for realizing associative memories. Compared with the existing neural networks for associative memories, the main advantage of the proposed model is that it has only one recurrent unit, which lowers the model complexity by the greatest extent. In the proposed neural network, each prototype pattern is stored in the connection weights between the input and hidden layers. In addition, the advanced performance of the proposed network is demonstrated by means of simulations of three numerical examples.