FPGA implementation of neuro-fuzzy system with improved PSO learning

Date

2016-07-01

Author

KARAKUZU, CİHAN
KARAKAYA, FUAT
Cavuslu, Mehmet Ali

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This paper presents the first hardware implementation of neuro-fuzzy system (NFS) with its metaheuristic learning ability on field programmable gate array (FPGA). Metaheuristic learning of NFS for all of its parameters is accomplished by using the improved particle swarm optimization (iPSO). As a second novelty, a new functional approach, which does not require any memory and multiplier usage, is proposed for the Gaussian membership functions of NFS. NFS and its learning using iPSO are implemented on Xilinx Virtex5 xc5vlx110-3ff1153 and efficiency of the proposed implementation tested on two dynamic system identification problems and licence plate detection problem as a practical application. Results indicate that proposed NFS implementation and membership function approximation is as effective as the other approaches available in the literature but requires less hardware resources.

Subject Keywords

Neuro-fuzzy network, FPGA implementation, VHDL, System identification, Metaheuristic learning

URI

https://hdl.handle.net/11511/66799

Journal

NEURAL NETWORKS

DOI

https://doi.org/10.1016/j.neunet.2016.02.004

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Unclassified, Article

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Citation Formats

C. KARAKUZU, F. KARAKAYA, and M. A. Cavuslu, “FPGA implementation of neuro-fuzzy system with improved PSO learning,” NEURAL NETWORKS, pp. 128–140, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66799.