Adaptive neuro fuzzy inference system for modeling of electronics devices: A review

Device modeling is to design model, which acts precisely like real device by giving same characteristics. Adaptive neuro-fuzzy inference system (ANFIS)[1] is one of the example of computational intelligence which can be used for modeling of electronics devices like BJT, FET etc. ANFIS is helpful for...

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Bibliographic Details
Published in2017 Innovations in Power and Advanced Computing Technologies (i-PACT) pp. 1 - 4
Main Authors Nagrare, Trupti H., Gurjar, A. A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2017
Subjects
Adaptation models
analyzing
ANFIS
circuit simulation
Computational modeling
device modeling
FPGA
Fuzzy logic
Integrated circuit modeling
Mathematical model
Neural networks
Training
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Summary:Device modeling is to design model, which acts precisely like real device by giving same characteristics. Adaptive neuro-fuzzy inference system (ANFIS)[1] is one of the example of computational intelligence which can be used for modeling of electronics devices like BJT, FET etc. ANFIS is helpful for mapping of input-output based on predetermined input output data pairs mostly in case of nonlinear function generation. In this paper study of various such devices, which are modeled using ANFIS, is shown. These models are then used in circuit simulation and also implemented in hardware i.e in FPGA. This modeling of devices enhances the speed of simulation, more reliability and also gives better time complexity.
DOI:10.1109/IPACT.2017.8245094