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

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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)
Volume7
Issue number8
DOIs
Publication statusPublished - 2018 Aug 11

Fingerprint

Nonlinear control systems
Field programmable gate arrays (FPGA)
Computer hardware description languages
Neural networks
Fuzzy rules
Membership functions
Temperature control
Learning algorithms
Integrated circuits
Entropy
Railroad cars
Polynomials
Hardware
Controllers
Water

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

Jhang, Jyun Yu ; Tang, Kuang Hui ; Huang, Chuan-Kuei ; Lin, Cheng Jian ; Young, Kuu Young. / FPGA implementation of a functional neuro-fuzzy network for nonlinear system control. In: Electronics (Switzerland). 2018 ; Vol. 7, No. 8.
@article{8deb371f44074c138ad38a35a29cdd71,
title = "FPGA implementation of a functional neuro-fuzzy network for nonlinear system control",
abstract = "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.",
author = "Jhang, {Jyun Yu} and Tang, {Kuang Hui} and Chuan-Kuei Huang and Lin, {Cheng Jian} and Young, {Kuu Young}",
year = "2018",
month = "8",
day = "11",
doi = "10.3390/electronics7080145",
language = "English",
volume = "7",
journal = "Electronics (Switzerland)",
issn = "2079-9292",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "8",

}

FPGA implementation of a functional neuro-fuzzy network for nonlinear system control. / Jhang, Jyun Yu; Tang, Kuang Hui; Huang, Chuan-Kuei; Lin, Cheng Jian; Young, Kuu Young.

In: Electronics (Switzerland), Vol. 7, No. 8, 145, 11.08.2018.

Research output: Contribution to journalArticle

TY - JOUR

T1 - FPGA implementation of a functional neuro-fuzzy network for nonlinear system control

AU - Jhang, Jyun Yu

AU - Tang, Kuang Hui

AU - Huang, Chuan-Kuei

AU - Lin, Cheng Jian

AU - Young, Kuu Young

PY - 2018/8/11

Y1 - 2018/8/11

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85052074311&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85052074311&partnerID=8YFLogxK

U2 - 10.3390/electronics7080145

DO - 10.3390/electronics7080145

M3 - Article

VL - 7

JO - Electronics (Switzerland)

JF - Electronics (Switzerland)

SN - 2079-9292

IS - 8

M1 - 145

ER -