Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Another K-winners-take-all analog neural network
IEEE Transactions on Neural Networks
A novel continuous-time neural network for realizing associative memory
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Synthesis of Brain-State-in-a-Box (BSB) based associative memories
IEEE Transactions on Neural Networks
K-winners-take-all circuit with O(N) complexity
IEEE Transactions on Neural Networks
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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.