Title
Design and implementation of an adaptive neuro-fuzzy inference system on an FPGA used for nonlinear function generation
Date Issued
01 December 2010
Access level
metadata only access
Resource Type
conference paper
Author(s)
Saldaña H.
SILVA CARDENAS, CARLOS BERNARDINO
Abstract
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. ©2010 IEEE.
Language
English
OCDE Knowledge area
Ingeniería eléctrica, Ingeniería electrónica
Scopus EID
2-s2.0-79952081746
ISBN of the container
9781424467419
Conference
2010 IEEE ANDESCON Conference Proceedings, ANDESCON 2010
Sources of information: Directorio de Producción Científica Scopus