Abstract
This paper presents a recurrent self-organizing map (RSOM) for temporal sequence processing. The RSOM uses the history of a pattern (i.e., the previous elements in the sequence) to compute the best matching unit and to adapt the weights of the map. The RSOM is similar to Kohonen's original SOM except that each unit has an associated recursive differential equation. The experimental results show that the RSOM is able to learn and distinguish temporal sequences, and that it can improve EEG-based epileptic activity detection.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.