FPGA Implementation of SVM for Nonlinear Systems Regression

This work resumes the previous implementations of Support Vector Machine for Classification and Regression and explicates the different methods and approaches adopted. Ever since the rarity of works in the field of nonlinear systems regression, an implementation of testing phase of SVM was proposed exploiting the parallelism and reconfigurability of Field-Programmable Gate Arrays (FPGA) platform. The nonlinear system chosen for application was a real challenging model: a fluid level control system existing in our laboratory. The implemented design with fixed point precision demonstrates good enough results comparing with the software performances based on the Normalized Mean Squared Error. Whereas, in term of computation time, a speed-up factor of 60 orders of time comparing to MATLAB results was achieved. Due to the flexibility of Xilinx System Generator, the design is capable to be reused for any other system with different data sets sizes and various kernel functions.

@article{SAYEHI2017,
  title     = {FPGA Implementation of SVM for Nonlinear Systems Regression},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {8},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Intissar SAYEHI and Mohsen MACHHOUT and Rached TOURKI},
  doi       = {10.14569/IJACSA.2017.080816},
  url       = {https://doi.org/10.14569/IJACSA.2017.080816}
}