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Design and implementation of an adaptive neuro-fuzzy inference system on an FPGA used for nonlinear function generation

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2 Author(s)
Henry José Block Saldaña ; Grupo de Microelectrónica, Pontificia Universidad Católica del Perú, Lima-Peru ; Carlos Silva Cárdenas

This paper presents a digital system architecture for a two-input one-output zero order ANFIS (Adaptive Neuro-Fuzzy Inference System) and its implementation on an FPGA (Field Programmable Gate Array) using VHDL (VHSIC Hardware Description Language). The designed system is used for nonlinear function generation. First, a nonlinear function is chosen and off-line training is carried out using MATLAB ANFIS to obtain the premise and consequence parameters of the fuzzy rules. Then, these parameters are converted to a binary fixed-point representation and are stored in read-only memories of the VHDL code. Finally, simulations are performed to verify the system operation and to evaluate the system response time for given input data.

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Date of Conference:

15-17 Sept. 2010