Location of Repository

Local Linear Model Tree (LOLIMOT) Reconfigurable Parallel Hardware

By A. Pedram, M. R. Jamali, T. Pedram, S. M. Fakhraie and C. Lucas

Abstract

other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms

Topics: Hardware, Neuro-Fuzzy Systems, Reconfigurable, Parallel
Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.193.6266
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.waset.org/journals/... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.