Associative memory in chaotic neural networks differs from traditional associative memory models in their rich dynamics that may lead to promising solutions for a number of information processing problems. In this paper, we propose a new method to control the chaotic neural network, whose refractoriness is tuned by using feedback control based on online averaging of network states. An augmented autoassociative layer is employed to further improve the retrieval performance. Simulation results demonstrate that the proposed chaotic neural network model gives favourable performance in handling noisy, incomplete and composite patterns, while in the meantime achieving either enhanced or comparable memory capacity compared with the conventional Hopfield net and other chaotic neural network models. With the ability to retrieve multiple patterns from memory according to their similarity, it is promising for the proposed network to be applied in real-world information retrieval tasks upon further improvement.
['Jeremiah D. Deng']
Controlling Chaotic Associative Memory for Multiple Pattern Retrieval