A generalized bipolar auto-associative memory model based on discrete recurrent neural networks

This paper presents a novel method for designing associative memories based on discrete recurrent neural networks to accurately memorize the networks׳ external inputs. In the method, a generalized model is proposed for bipolar auto-associative memory and establishing an exponential stable criteria of the networks. The model is of generality with considering time delay and introducing a tunable slope activation function, and can robustly recall the memorized external input patterns in an auto-associative way. Experimental verification demonstrates that the proposed method is more effective and generalized than other existing ones.