Design and analysis of associative memories based on external inputs of delayed recurrent neural networks

This paper presents two new design procedures for synthesizing autoassociative memory and heteroassociative memory based on recurrent neural networks with different external inputs and mixed delays. Sufficient criteria are established to guarantee the global exponential stability for recurrent neural networks with mixed delays. The design procedures are dependent on external inputs, which ensure to restore accurate memories. The stored patterns are associated with their retrieval probes internally in a robust and fault tolerant way. The obtained results improve and extend some related works. Two illustrative examples are given to verify the effectiveness of the theoretical results.