A fast fixed point learning method to implement associative memory on CNNs

Cellular Neural Networks (CNNs) with space-varying interconnections are considered here to implement associative memories. A fast learning method is presented to compute the interconnection weights. The algorithm was carefully tested and compared to other methods. Storage capacity, noise immunity, and spurious state avoidance capability of the proposed system are discussed.