A novel chaotic hetero-associative memory

In this study, a novel hetero-associative memory with dynamic behavior is proposed. The proposed hetero-associative memory can store as twice as a regular hetero-associative memory using a new extension of sparse learning method. The new learning method gives the network ability of successive learning, therefore it can store new patterns even after learning phase. In other words, learning step and recall step are not separated in this method. We also add chaos searching in recall step in order to make the network be able to converge into the best possible solution among whole search space. Chaotic behavior helps the network jumps from local minimums. Simulation result shows higher storage capacity and also better recall performance in comparison with regular hetero-associative memory with the presence of noisy input data.