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Electronics 2018, 7(8), 145; https://doi.org/10.3390/electronics7080145

FPGA Implementation of a Functional Neuro-Fuzzy Network for Nonlinear System Control

1
Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu City 300, Taiwan
2
Department of Industrial Education and Technology, National Changhua University of Education, Changhua County 500, Taiwan
3
Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung City 406, Taiwan
4
Department of Computer Science & Information Engineering, National Chin-Yi University of Technology, Taichung City 406, Taiwan
*
Author to whom correspondence should be addressed.
Received: 30 May 2018 / Revised: 9 August 2018 / Accepted: 9 August 2018 / Published: 11 August 2018
(This article belongs to the Special Issue Selected Papers from the IEEE ICASI 2018)

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. View Full-Text
Keywords: neuro-fuzzy networks; entropy; gradient descent; functional link neural networks; Field Programmable Gate Array (FPGA); control neuro-fuzzy networks; entropy; gradient descent; functional link neural networks; Field Programmable Gate Array (FPGA); control
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Jhang, J.-Y.; Tang, K.-H.; Huang, C.-K.; Lin, C.-J.; Young, K.-Y. FPGA Implementation of a Functional Neuro-Fuzzy Network for Nonlinear System Control. Electronics 2018, 7, 145.

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