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An Implementation of the Neuro-fuzzy Inference Circuit
First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06)
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In this paper, we propose a neuro-fuzzy inference circuit applicable to realtime learning. We adopted a back-propagation algorithm used in the neural network for learning. ...
Through the experimental use of the Field Programmable Gate Array (FPGA), we confirm that a high speed self-tuning of the fuzzy inference rules can be realized on the circuit. ...
Therefore, we have developed one of the neuro-fuzzy learning algorithms [12] designed to prevent the case of weak-firing or non-firing even after learning. However, since the ...
doi:10.1109/icicic.2006.232
dblp:conf/icicic/FujimotoSMYM06
fatcat:bjwhl4typbhbddnsl4tiekqvza
An asynchronous mixed-mode neuro-fuzzy controller for energy efficient machine intelligence SoC
2011
IEEE Asian Solid-State Circuits Conference 2011
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This paper presents an asynchronous digitalanalog mixed-mode neuro-fuzzy controller that enables energy efficient implementation of machine intelligence SoC. ...
The proposed neuro-fuzzy controller adopts an asynchronous 2stage pipeline for analog and digital domain operations, which is the main contributor to high throughput and energy efficient machine intelligence ...
Neuro-fuzzy algorithm, or an integrated inference system of neural network and fuzzy logic, is one of the most popular MI due to its high functionality and scalability. ...
doi:10.1109/asscc.2011.6123598
dblp:conf/asscc/OhKY11
fatcat:fomvldsanfgype27gr7wiu6je4
1.15mW mixed-mode neuro-fuzzy accelerator for keypoint localization in image processing
2011
2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS)
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To reduce processing time of keypoint localization with low power consumption, analog Adaptive Neuro-Fuzzy Inference System (ANFIS) and digital controller are implemented together. ...
Compared to the conventional digital standalone system, O.733mm 2 neuro-fuzzy accelerator achieves 43% processing time reduction and also results in 19.4% time reduction of image feature extraction process ...
With high programmability of digital learning block, most of the neuro-fuzzy layers of ANFIS system are implemented with analog circuit. ...
doi:10.1109/mwscas.2011.6026495
fatcat:va4vprisczfcnpuu5eduj5mucm
A 1.2mW on-line learning mixed mode intelligent inference engine for robust object recognition
2010
2010 Symposium on VLSI Circuits
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It contains analog digital mixed mode neuro-fuzzy circuits for the on-line learning to increase attention efficiency. ...
In addition, it is implemented by combining analog neuro-fuzzy circuits and a digital controller to reduce the area and power consumption with high speed operation, and its detail architecture will be ...
It receives scale, orientation, and motion as inputs and generates the object confidence through the neuro-fuzzy inference. ...
doi:10.1109/vlsic.2010.5560256
fatcat:iogah5z3e5el7pqq6nut4ality
Embedded Adaptive Neuro Fuzzy Inference System with Hardware Implemented Real Time Parameter Update
2015
MACRo 2015
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The block diagram of the neuro-adaptive inference system output computing implemented in hardware is discussed, and the implementation in reconfigurable circuit of real-time parameter tuning is presented ...
In the paper a Sugeno architecture based hardware implemented neuro adaptive inference system's training algorithm is presented. ...
Research Center for Development and Market Introduction of Advanced Information and Communication Technologies. ...
doi:10.1515/macro-2015-0021
fatcat:rxw4fxdcvvbvdgt676jne62t4u
Analysis of Regenerative Braking In Brushless Dc Motor Drive Using Adaptive Neuro Based Fuzzy Inference System
2015
International Journal of Science and Research (IJSR)
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has been implemented which has the ability to recover energy An adaptive neuro fuzzy inference system is developed using MATLAB. ...
The proposed system includes Brushless DC motor control utilizing the PID control, and improved performance via adaptive neuro based fuzzy control. ...
It implements a Sugeno fuzzy inference system for a systematic approach to generating fuzzy rules from a given input output dataset. ...
doi:10.21275/v4i12.nov152004
fatcat:yvuhxrndv5h3dmtipsba5ne5uq
A Neuro-Fuzzy Technique for Implementing the Half-Adder Circuit Using the CANFIS Model
[article]
2012
arXiv
pre-print
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This paper presents a technique for the implementation of the Half-adder circuit using the CoActive Neuro-Fuzzy Inference System (CANFIS) Model and attempts to solve the problem using the NeuroSolutions ...
A Neural Network, in general, is not considered to be a good solver of mathematical and binary arithmetic problems. However, networks have been developed for such problems as the XOR circuit. ...
in the area of the development of neuro-fuzzy computers [6] that can be used as an intelligent replacement of current computers. ...
arXiv:1209.4895v1
fatcat:bbwjhblvkbf57doefbb3zedyrq
Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware Implementation
[article]
2011
arXiv
pre-print
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Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. ...
In this paper a novel neuro-fuzzy system is proposed where its learning is based on the creation of fuzzy relations by using new implication method without utilizing any exact mathematical techniques. ...
However, eventually all of these neuro-fuzzy systems are trying to approach an ideal soft-computing tool where the nature of its computing or inference be as similar as possible to computation and inference ...
arXiv:1103.1156v1
fatcat:i5plmdb4crfmbjqagnzsrbguwi
A neuro-fuzzy approach for tracking maximum power point of photovoltaic solar system
2021
International Journal of Power Electronics and Drive Systems (IJPEDS)
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artificial neural network-fuzzy (neuro-fuzzy). ...
After that, the fuzzy logic controller (FLC) with rule inference begins to establish the photovoltaic solar system at the MPP. ...
Therefore, we must first build the neural network that is preparing a learning base and learn the network, then implement this neural network in the control circuit, followed by fuzzy logic controller. ...
doi:10.11591/ijpeds.v12.i2.pp1252-1264
fatcat:ilc4lap6mbhyxlhilq7fzghlm4
G4-FET modeling for circuit simulation by adaptive neuro-fuzzy training systems
2012
IEICE Electronics Express
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The accuracy of the proposed model is verified by HSPICE circuit simulations. ...
G4-FET has attracted attention as an emerging device for the future generations of semiconductor industry. ...
Adaptive neuro-fuzzy training system ANFIS is an adaptive network which combines the application of neural network and fuzzy logic. ...
doi:10.1587/elex.9.881
fatcat:y7fm6ccv5jfzvj7c4435dnnzsa
Design and Implementation of ANFIS Algorithm Using VHDL for Vechicular System
2015
International Journal on Recent and Innovation Trends in Computing and Communication
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In this paper an (Adaptive Neuro Fuzzy(NF) Inference System algorithm)ANFISA proposed and try to implemented using FPGA(FIELD PROGRAMMABLE GATE ARRAY)for the behavior of the system will be nonlinear. ...
But in this paper the implementation of t he (ANFIS)adaptive neuro fuzzy inference system algorithm using FPGA boards has been try to investigated in this work. ...
Adaptive neuro fuzzy inference system is very useful and handles nonlinearity effectively. ...
doi:10.17762/ijritcc2321-8169.150284
fatcat:36gzwr6mubcb7lcxdahkhkyony
Comparison of Fuzzy and Neuro-Fuzzy Controllers for Maximum Power Point Tracking of Photovoltaic Modules
2016
The Renewable Energies and Power Quality Journal (RE&PQJ)
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and the neuro-fuzzy control method. ...
The paper make a comparison among two control methods for maximum power point tracking (MPPT) of a photovoltaic (PV) system under varying irradiation and temperature conditions: the fuzzy control method ...
Acknowledgement This work was realized through the Partnership program in priority domains -PN II, developed with support from ANCS CNDI -UEFISCDI, project no. PN-II-PT-PCCA-2011-3.2-1670. ...
doi:10.24084/repqj14.465
fatcat:hw47dsqldzakjg5bezixudxu4y
The Neuro-Fuzzy Computing System With the Capacity of Implementation on a Memristor Crossbar and Optimization-Free Hardware Training
2014
IEEE transactions on fuzzy systems
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2012 pre-print arXiv:1211.6205v1 fatcat:z4y35bnf6zf6hgapep3w7uz5sa
In this paper, first we present a new explanation for the relation between logical circuits and artificial neural networks, logical circuits and fuzzy logic, and artificial neural networks and fuzzy inference ...
Then, based on these results, we propose a new neuro-fuzzy computing system which can effectively be implemented on the memristor-crossbar structure. ...
Interpretation of ANNs as fuzzy inference systems The aim of this section is to provide an answer to the following questions from a new point of view: (1) How similar is the behavior of ANNs to fuzzy inference ...
doi:10.1109/tfuzz.2013.2290140
fatcat:us6ruowocvbpvadcnja3umsone
Hardware Implementation of a Neuro-Fuzzy Controller Using High Level Synthesis Tool
2015
MACRo 2015
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The purpose of this work is to present the design flow and the implementation of a neuro-fuzzy controller Intellectual Property (IP) core, using High Level Synthesis (HLS) tool. ...
The realized IP core is designed for FPGA based embedded system architectures. The implemented control algorithm is a Sugeno model based Adaptive Neuro-Fuzzy Inference System (ANFIS). ...
Acknowledgements This publication/research has been supported by the European Union and Hungary and co-financed by the European Social Fund through the project TÁMOP-4. ...
doi:10.1515/macro-2015-0018
fatcat:fwrg37tdofbtvp3m6djt3jkyyq
Applications of neuro fuzzy systems: A brief review and future outline
2014
Applied Soft Computing
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Neuro-fuzzy systems refer to combinations of artificial neural network and fuzzy logic in the field of artificial intelligence, which was proposed by Jang [1] in 1993. ...
This paper surveys neuro fuzzy systems (NFS) development using classification and literature review of articles for the last decade (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) to ...
Acknowledgment The authors wish to express sincere gratitude to the anonymous reviewers for their constructive comments and helpful suggestions, which lead to substantial improvements of this paper. ...
doi:10.1016/j.asoc.2013.10.014
fatcat:iochb6rlgbhb5dmpmx7jeh54mu
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