| Summary: | 碩士 === 朝陽科技大學 === 資訊工程系碩士班 === 96 === This study presents the hardware implementations of functional-link-based neuro-fuzzy network (FLNFN) using Xilinx Field Programmable Gate Arrays (FPGAs) for solving nonlinear control problems. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. The FLNFN model can generate the consequent part of a nonlinear combination of input variables. Thus, the designed can improve the accuracy of functional approximation. The learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN.
In order to obtain the high speed operation and the real-time application, we use very high speed integrated circuit hardware description language (VHDL) to design FLNFN controller and implemented on FPGA. Finally, we confirmed the viability of this implementation through experiments of the control of water bath temperature system and control of backing up the truck.
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