Associative Memory for Online Learning in Noisy Environments Using Self-Organizing Incremental Neural Network

@article{Sudo2009AssociativeMF,
  title={Associative Memory for Online Learning in Noisy Environments Using Self-Organizing Incremental Neural Network},
  author={Akihito Sudo and Akihiro Sato and Osamu Hasegawa},
  journal={IEEE Transactions on Neural Networks},
  year={2009},
  volume={20},
  pages={964-972}
}
Associative memory operating in a real environment must perform well in online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these requirements. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases… CONTINUE READING