FPGA implementation of a functional neuro-fuzzy network for nonlinear system control

Jyun Yu Jhang, Kuang Hui Tang, Chuan Kuei Huang, Cheng Jian Lin*, Kuu-Young Young

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This study used Xilinx Field Programmable Gate Arrays (FPGAs) to implement a functional neuro-fuzzy network (FNFN) for solving nonlinear control problems. A functional link neural network (FLNN) was used as the consequent part of the proposed FNFN model. This study adopted the linear independent functions and the orthogonal polynomials in a functional expansion of the FLNN. Thus, the design of the FNFN model could improve the control accuracy. The learning algorithm of the FNFN model was divided into structure learning and parameter learning. The entropy measurement was adopted in the structure learning to determine the generated new fuzzy rule, whereas the gradient descent method in the parameter learning was used to adjust the parameters of the membership functions and the weights of the FLNN. In order to obtain high speed operation and real-time application, a very high speed integrated circuit hardware description language (VHDL) was used to design the FNFN controller and was implemented on FPGA. Finally, the experimental results demonstrated that the proposed hardware implementation of the FNFN model confirmed the viability in the temperature control of a water bath and the backing control of a car.

Original languageEnglish
Article number145
JournalElectronics (Switzerland)
Issue number8
StatePublished - 11 Aug 2018


  • Control
  • Entropy
  • Field Programmable Gate Array (FPGA)
  • Functional link neural networks
  • Gradient descent
  • Neuro-fuzzy networks


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