Discrete-Time Cellular Neural Networks for Associative Memories: A New Design Method via Iterative Learning and Forgetting Algorithms

摘要

In this paper a synthesis procedure of Discrete-Time Cellular Neural Networks (DTCNN's) for associative memories with iterative learning and forgetting algorithms is developed, by which each pattern to be stored is learnt one at a time and each pattern to be forgotten is deleted one at a time. The proposed approach exploits the properties of pseudo-Inverse matrices and preserves the local connection feature of DTCNN's.