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.
Highlights
Neural fuzzy networks (NFNs) have been widely applied in various fields [1,2,3]
To verify the control performance of the functional neuro-fuzzy network (FNFN), two control problems were tested in the experiments
The FNFN controller was used for solving the temperature control of a water bath [11]
Summary
Neural fuzzy networks (NFNs) have been widely applied in various fields [1,2,3]. Traditional NFNs combine neural networks to learn from processes with fuzzy reasoning to handle uncertain information.These can only be applied to parameter learning based on the ordered derivative algorithm where the structure of the NFNs has been determined and fixed in advance [4,5,6]. Neural fuzzy networks (NFNs) have been widely applied in various fields [1,2,3]. Traditional NFNs combine neural networks to learn from processes with fuzzy reasoning to handle uncertain information. These can only be applied to parameter learning based on the ordered derivative algorithm where the structure of the NFNs has been determined and fixed in advance [4,5,6]. For TSK-type neural fuzzy networks (TNFNs), the consequent part of each fuzzy rule is a linear combination of the input variable. The traditional TNFN cannot use the mapping capabilities of the linear function combination in consequent parts of the fuzzy rules. The FNFN model, which combines a neuro-fuzzy network with a FLNN [9], was proposed to improve
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