Chaotic Associative Memory for Sequential Patterns

Yuko Osana, Masafumi Hagiwara

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)


In this paper, we propose a Chaotic Associative Memory for Sequential Patterns (CAMSP). The proposed CAMSP is based on a Chaotic Associative Memory (CAM) composed of chaotic neurons. In the conventional chaotic neural network, when a stored pattern is given to the network as an external input continuously, around the input pattern is searched. The CAM makes use of this property in order to separate superimposed patterns. In this research, the CAM is applied to associations for sequential patterns. The proposed model has the following features: (1) it can deal with associations for the sequential patterns; (2) it can realize associations considering patterns' history; (3) it is robust for noisy input. A series of computer simulations shows the effectiveness of the proposed model.

Original languageEnglish
Number of pages6
Publication statusPublished - 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: 1999 Jul 101999 Jul 16


OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


Dive into the research topics of 'Chaotic Associative Memory for Sequential Patterns'. Together they form a unique fingerprint.

Cite this