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Neuro-fuzzy modeling and control

J.-S.R. Jang, Chuen-Tsai Sun
1995 Proceedings of the IEEE  
Abstract| F undamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed.  ...  We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed.  ...  Each design method for neuro-fuzzy controllers corresponds to a way o f obtaining the control action these methods are discussed next. A.  ... 
doi:10.1109/5.364486 fatcat:q2tq6xv4xbbsbo3nowrnov45pe


Agustín I. Cabrera, Guadalupe Ramírez, Gonzalo Gálvez
2005 IFAC Proceedings Volumes  
The neuro fuzzy system based on two independent structures is described, the first a neuro-observer system developed by use of dynamical neural networks, and the second as the control system based on fuzzy  ...  Besides, the neuro-fuzzy system performance is proved by the application to the Bergman th blood Insulin-Glucose interaction model, the simulations show the neuro-fuzzy output as the insulin infusor output  ...  Neural Network like neuro-observer (Cabrera et al. 2003 ) and the Fuzzy system like a fuzzy control.  ... 
doi:10.3182/20050703-6-cz-1902.02154 fatcat:aeaj2rdjbjgudcqrpyr2nl5gv4

Multirobot convoying using neuro-fuzzy control

Kim C. Ng, M.M. Trivedi
1996 Proceedings of 13th International Conference on Pattern Recognition  
In this paper, real-time implementation of multirobot convoying behavior utilizing neuro-fuzzy control is presented.  ...  [10] has implemented fuzzy control on this similar problem.  ...  The work discussed in this paper is focusing on the motion control using the proposed Neural Integrated Fuzzy conTroller (NiF-T).  ... 
doi:10.1109/icpr.1996.547600 dblp:conf/icpr/NgT96 fatcat:einjrv2jknhgppjkrpwul2dgiq

Neuro-fuzzy controller for control and robotics applications

D.H. Rao, M.M. Gupta
1994 Engineering applications of artificial intelligence  
The purpose of this paper is to develop a neuro-fuzzy controller (NFC) for adaptive tracking in unknown nonlinear dynamic systems, and for on-line computation of inverse kinematic transformations of robot  ...  The fuzzy logic controller (FLC), based on fuzzy set theory, provides a means for converting a linguistic control strategy into control actions and thus offering a high level of computation.  ...  This paper describes a neuro-fuzzy controller (NFC) that combines a fuzzy logic controller (FLC) and a recurrent neural network (RNN) for control and robotics applications.  ... 
doi:10.1016/0952-1976(94)90027-2 fatcat:ndryj6a44ndebfpwdmwe327zfy

Neuro-Fuzzy Implementation of a Self-Tuning Fuzzy Controller

R. K. Mudi, Chanchal Dey, T. T. Lee
2006 2006 IEEE International Conference on Systems, Man and Cybernetics  
Effectiveness of the developed neuro-fuzzy controllers (NFPIC-1 and NFPIC-2) is demonstrated using second-order linear as well as nonlinear processes.  ...  We consider two different structures of the proposed neuro-fuzzy PI controller (NFPIC); called NFPIC-1 and NFPIC-2, having only 50 and 49 rules respectively against 98 original rules of STFPIC.  ...  CONCLUSION We developed two neuro-fuzzy control structures, NFPIC-1 and NFPIC-2 with similar performance as that of a previously designed self-tuning fuzzy controller (STFPIC).  ... 
doi:10.1109/icsmc.2006.385111 dblp:conf/smc/MudiDL06 fatcat:ybsbkeo2tjdwxk3yonkepvdcsi

Brushless DC Motor Speed Control using PID Controller, Fuzzy Controller, and Neuro Fuzzy Controller

Ahmed K., Mohammed S., Mohamed S., Fayez F.
2018 International Journal of Computer Applications  
The purpose of this paper is to control the speed of a brushless dc motor by using PID controller, Fuzzy logic controller, and Neuro fuzzy controller.  ...  According to these varieties of control techniques which used to control the speed, we have many parameters which used to assess that which controller will be better to use.  ...  ripples has decreased slightly to 2.29% so we can conclude that Neuro Fuzzy controller is better than Fuzzy controller.  ... 
doi:10.5120/ijca2018916783 fatcat:dp52ewajgrauhaa4kx65ps6dcy

Neuro-Fuzzy Control of a Robotic Manipulator

P. Gierlak, M. Muszyńska, W. Żylski
2014 International Journal of Applied Mechanics and Engineering  
The purpose of the neuro-fuzzy system is the approximation of the nonlinearity of the robotic manipulator's dynamic to generate a compensatory control.  ...  This system is understood as a hybrid controller, being a combination of fuzzy logic and an artificial neural network.  ...  Neuro-fuzzy nonlinearity compensator The neuro-fuzzy system was used for approximation of nonlinear functions.  ... 
doi:10.2478/ijame-2014-0039 fatcat:uxth2k7xubflzahyfjt4hcugry

Optimal Neuro-Fuzzy D.C. Motor Speed Control

Assist lecture. Hameed A, Assist lecture. Hameed A
2013 Al-Rafidain Engineering Journal  
Because of the lack of a clear and a known way for selecting the type and number of membership function in case of fuzzy control, An Adaptive Neuro-Fuzzy Inference System which comprises a fuzzy inference  ...  Aneuro-Fuzzy controller with random number and type of membership function is designed to control the speed of the d.c. motor as a closed loop system.  ...  ‫ٌزٌٛ١ذ‬ ‫اٌغ١بساد‬ ‫فٟ‬ ‫اٌىٙشثبئ١خ‬ ‫اٌمدذسح‬ ‫ِؾطبد‬ ‫فٟ‬ ‫وٛٔٙب‬ ‫ػٓ‬ ‫فؼال‬ , ً ‫إ‬ ‫اٌجبؽض١ٓ‬ ِٓ ‫وض١ش‬ ‫رطشق‬ ‫رظّ١ُ‬ ٌٝ ( ‫اٌزى١ف١خ‬ ‫اٌؼظج١خ‬ ‫اٌشجىبد‬ ‫ػٍٝ‬ ‫٠غزٕذ‬ ‫ػجبثٟ‬ ‫ِغ١طش‬ Neuro-Fuzzy  ... 
doi:10.33899/rengj.2013.72801 fatcat:wpcze4rgwvgotnjchttramcana

A Genetic based Neuro-Fuzzy Controller System

Mohamed Mohamed, A. H
2014 International Journal of Computer Applications  
Designing the controller of the mobile robot is a very complex task. Many simple control systems used the neuro-fuzzy controller in the mobile robots.  ...  The proposed system introduces the uses of the genetic algorithm for optimizing the parameters of the neuro-fuzzy controller. So, the proposed system can improve the performance of the mobile robots.  ...  The present research introduces a new methodology that can optimize neuro-fuzzy controller system.  ... 
doi:10.5120/16306-5532 fatcat:luykafqbtngddoe5bwto7qs74q

Attitude Control of Quadcopter Using Adaptive Neuro Fuzzy Control

Asif Sajjad Khan Anjum, Rana Ali Sufian, Zain Abbas, Ijaz Mansoor Qureshi
2016 International Journal of Hybrid Information Technology  
This research contains the simulation and designing of Quadcopter using Adaptive Neuro Fuzzy Controller to control the altitude of quadcopter and obstacle detection.  ...  This research work used a Fuzzy controller to control the pitch angle of quadcopter and avoiding obstacles.  ...  In this research, a new technique of Adaptive Neuro Fuzzy Controller for Quadcopter to make it robust, versatile and produce a low error steady state.  ... 
doi:10.14257/ijhit.2016.9.4.13 fatcat:bn7y33rouvaqjcy46czxen5a6e

Power Transformer Control by Neuro Fuzzy Controller and Haar Wavelet Transform

2016 International Journal of Science and Research (IJSR)  
The aneural network based fuzzy logic controller is used to design protection relay for transformer. The simulation is done by MATLAB/SIMULINK software and results are shown clearly in this paper.  ...  This paper proposed novel control technique for transformer protection.This protection approach is based on extracting the fundamental components present in differential currents.This paper aims to prove  ...  Both the architecture and the learning algorithm are presented for a general Neuro fuzzy controller. From this general Neuro fuzzy controller, a proportional Neuro fuzzy controller is derived.  ... 
doi:10.21275/v5i4.nov162736 fatcat:juhis5f5nzfvtfg7r4ana2j6jy

Hierarchical Fuzzy Signature and Neuro-Fuzzy Hybrid System for QMS Control

Raouf Ketata, National Institute of Applied Science and Technology (INSAT), Hajer Ben Mahmoud, Hela Lassoued
2021 International Journal of Emerging Technology and Advanced Engineering  
Keywords—Quality management system, Fuzzy logic, Neuro-Fuzzy, hierarchical structure, fuzzy signature  ...  Hence, to provide the control of QMS problem insurance, two approaches are investigated which are Hierarchical Fuzzy Signature (HFS) and NeuroFuzzy Hierarchical Hybrid system (NFHH), respectively.  ...  Figure 2 shows the HFS structure used for QMS control. B. Hierarchical Neuro-Fuzzy Hybrid System Neural Network (NN) and Fuzzy Inference System (FIS) paradigms are frequently applied together.  ... 
doi:10.46338/ijetae1121_17 fatcat:twltsnenpvb3ll7s32ld3ftw4q


M.N.H. Siddique, M.O. Tokhi
2002 IFAC Proceedings Volumes  
A typical method for rule reduction of a PID fuzzy controller is to divide the three-term into two separate PD and PI parts.  ...  This means that the fuzzy sets for change of error and sum of error will be redefined within the same universe of discourse, i.e., the fuzzy sets for both change of error and sum of error will be the same  ...  After training the neural network by genetic algorithms, the scaling factors Fig. 2 . 2 Block diagram of neuro-fuzzy controller.  ... 
doi:10.3182/20020721-6-es-1901.00969 fatcat:mh3vht73yfdddpjbmlmgdwen4y


Sergio E. Pinto Castillo, Mike J. Grimble, Reza Katebi
2005 IFAC Proceedings Volumes  
The development of a Self-Tuning Neuro-Fuzzy Generalized Minimum Variance (GMV) controller is described.  ...  The controller is formulated in a polynomial system approach mixed with a Neuro-Fuzzy model and Fuzzy Self-Tuning mechanism.  ...  This controller is based on a polynomial system approach mixed with the Neuro-Fuzzy (NF) model and the Fuzzy Self-Tuning Mechanism.  ... 
doi:10.3182/20050703-6-cz-1902.01095 fatcat:xrdtxawdcvetzagbcojwaajyfy

Adaptive neuro-fuzzy controller of switched reluctance motor

Ahmed Tahour, Hamza Abid, Ghani Aissaoui
2007 Serbian Journal of Electrical Engineering  
An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated.  ...  This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed.  ...  Adaptive Neuro-Fuzzy MODE Speed Controller Adaptive neuro-fuzzy principle A typical architecture of an ANFIS is shown in Fig. 3 , in which a circle indicates a fixed node, whereas a square indicates  ... 
doi:10.2298/sjee0701023t fatcat:d3fuqreqnvakhjl5ut33m4vv6a
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