Abstract: The main difference between the recall rule of recurrent correlation associative memory and that of Hopfield network is that RCAM operates a nonlinearity upon the correlations between input and stored patterns. In this paper, it proposes to improve the performance of RCAM by truncating the insignificant correlations, produce a novel associative memory based on the architecture of RCAM, noted as TRCAM. The theoretical analysis shows that the method raises the signal-to-noise ratio greatly. The simulations illustrate that this model enjoys a high capacity Back

Key words: associative memory, capacity, error-correcting capability, neural networks