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Tools for intelligent control: fuzzy controllers, neural networks and genetic algorithms

M. Jamshidi
2003 Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences  
Other elements of soft computing, such as neural networks and genetic algorithms, are also treated for the novice reader.  ...  Fuzzy sets and fuzzy logic and their applications to control systems have been documented.  ...  (f ) Stability of fuzzy control systems One of the most important issues in any control-system analysis-fuzzy or otherwise-is stability.  ... 
doi:10.1098/rsta.2003.1225 pmid:12952685 fatcat:qatleeohgbdwfc4u65hk63jm7a

Experiences with fuzzy logic and neural networks in a control course

F. Jurado, M. Castro, J. Carpio
2002 IEEE Transactions on Education  
In this paper, fuzzy logic controllers have been developed using speed and mechanical power deviations, and a neural network has been designed to tune the gains of the fuzzy logic controllers.  ...  Control system education must include experimental exercises that complement the theory presented in lectures. These exercises include modeling, analysis, and design of a control system.  ...  It describes a specific neural network technique that has been developed and applied to the problem of tuning fuzzy controllers.  ... 
doi:10.1109/te.2002.1013881 fatcat:jvpj2tpoyfcfpm4cpz5xek57nq

Missile Control Using Fuzzy Cerebellar Model Arithmetic Computer Neural Networks

Z. Jason Geng, Claire L. McCullough
1997 Journal of Guidance Control and Dynamics  
., “Machine Tool Active Chatter Control Using Fuzzy CMAC Neural Networks,” 2nd S.M.Wu Symposium on Manufacturing Science (Ann Arbor, MI), American Society of Mechanical Engineers, New York, 1996, p. 134  ...  This fuzzy neural network architecture exploits a synergism between the original CMAC neural network and the theory of fuzzy logic controller.  ... 
doi:10.2514/2.4077 fatcat:hltjtnedyfbwnkzlxrhbpfgave

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

M. Bazazzadeh, H. Badihi, A. Shahriari
2011 International Journal of Aerospace Engineering  
This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC) for a specific Jet Engine.  ...  At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine  ...  The problems of FLC stability analysis and optimality are not addressed explicitly; such issues are still open problems in fuzzy controller design.  ... 
doi:10.1155/2011/156796 fatcat:vttjnwdscffdtlyq73ryhg6lqq

Model Reference Adaptive Control and Fuzzy Neural Network Synchronous Motion Compensator for Gantry Robots

Chin-Sheng Chen, Nien-Tsu Hu
2021 Energies  
A model reference adaptive control and fuzzy neural network (FNN) synchronous motion compensator for a gantry robot is presented in this paper.  ...  Then, a fuzzy neural network compensator for the gantry robot is proposed to compensate for the synchronous errors between the dual servo motors to improve precise movement.  ...  An FNN is a network with fuzzy inference characteristics implemented by a four-layer neural network.  ... 
doi:10.3390/en15010123 fatcat:x7lxqs4565bw3f4eigpgybiyua

A Review of Fuzzy Logic and Neural Network Based Intelligent Control Design for Discrete-Time Systems

Yiming Jiang, Chenguang Yang, Hongbin Ma
2016 Discrete Dynamics in Nature and Society  
Over the last few decades, the intelligent control methods such as fuzzy logic control (FLC) and neural network (NN) control have been successfully used in various applications.  ...  For discrete-time FLC systems, numerous remarkable design approaches are introduced and a series of efficient methods to deal with the robustness, stability, and time delay of FLC discrete-time systems  ...  [105] also studied the ADP for discrete-time systems to obtain the -optimal control by using neural networks.  ... 
doi:10.1155/2016/7217364 fatcat:qkjxrfnvpja4zjzk2cgmg6x7tm

Production Control Process using Integrated Robust Data Envelopment Analysis and Fuzzy Neural Network

Hadi Gholizadeh, Hamed Fazlollahtabar
2019 International journal of mathematical, engineering and management sciences  
Also, integrated fuzzy-neural network and data envelopment analysis is used for optimization and analysis purposes.  ...  Therefore, in this study, the parameters for controlling the process of forming are optimized to reduce the cycle time.  ...  Acknowledgement Authors appreciate the reviewers and the respected editor of IJMEMS for their constructive comments leading to strengthen the quality and presentation of the paper.  ... 
doi:10.33889//ijmems.2019.4.3-046 fatcat:amuqq3c2svgcph4xsps5j5d7yi

Suppression of Wing Rock Using Artificial Neural Networks and Fuzzy Logic Controller

A. G. Sreenatha, P. P. Wong, J. Y. Choi
2004 Journal of Aircraft  
Suppression of Wing Rock Using Artificial Neural Networks and Fuzzy Logic Controller A. G. Sreenatha* and P. P.  ...  As such, intelligent systems such as artificial neu- ral networks (ANN) and fuzzy controllers have become popular design paradigms for wing rock control.  ... 
doi:10.2514/1.6848 fatcat:563zc5pkhffxbggvglcfbilnvy

Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

Emad M. Natsheh, Alhussein Albarbar
2013 Smart Grid and Renewable Energy  
The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.  ...  The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels.  ...  Hence, this paper presents an adaptive management strategy for power flow in stand-alone hybrid power systems based on fuzzy logic and neural network.  ... 
doi:10.4236/sgre.2013.42023 fatcat:fmnh4eqbzjfkzimne6vd7smolu

High efficiency fault-detection and fault-tolerant control approach in Tennessee Eastman process via fuzzy-based neural network representation

M. Adeli, A. H. Mazinan
2019 Complex & Intelligent Systems  
We looked at the background of fault-detection and fault-tolerant control algorithms to propose a new high efficiency one with a focus on Tennessee Eastman process through fuzzy-based neural network representation  ...  It should be noted that the proposed implementation tools are taken into real consideration as the fuzzy-based neural network representation.  ...  The total cost of raw materials used in input lines, purge stream, and output line or wasted is calculated.  ... 
doi:10.1007/s40747-019-0094-3 fatcat:lnonf5mrcnhstli5wctmsr67ii

Fuzzy bio-interface: Indicating logicality from living neuronal network and learning control of bio-robot

Isao Hayashi, Megumi Kiyotoki, Ai Kiyohara, Minori Tokuda, Suguru N. Kudoh
2011 The 2011 International Joint Conference on Neural Networks  
We believe that the framework of fuzzy system is essential for BCI and BMI, thus name this technology "fuzzy bio-interface". We show the usefulness of a fuzzy bio-interface through some examples.  ...  determines a control process.  ...  By the Schweizer t − norm and t − conorm operator, we formulate a new algorithm for analyzing logicality and connectivity of the living neuronal networks.  ... 
doi:10.1109/ijcnn.2011.6033532 dblp:conf/ijcnn/HayashiKKTK11 fatcat:czdg27kuqjaq3ibus3a3qq6w4i

Adaptive MIMO Controller Design for Chaos Synchronization in Coupled Josephson Junctions via Fuzzy Neural Networks

Thien Bao Tat Nguyen
2017 Journal of Advanced Engineering and Computation  
The fuzzy neural controller employs a fuzzy neural network to simulate the behavior of the ideal feedback linearization controller, while the sliding mode controller is used to ensure the robustness of  ...  The developed controller has two parts: the fuzzy neural controller and the sliding mode controller.  ...  Nowadays, fuzzy logic and neural networks are used as the power tools for modelling and controlling highly uncertain, nonlinear and complex systems [12] , [13] , [14] , [15] , [16] .  ... 
doi:10.25073/jaec.201711.52 fatcat:njlfarmktbd5hd3inkrtiz6zmm

Robust control for vibration control systems with dead-zone band and time delay under severe disturbance using adaptive fuzzy neural network

Do Xuan Phu, Van Mien
2020 Journal of the Franklin Institute  
The controller is formulated based on a type-2 fuzzy neural network integrating with a new modification of Riccati-like equation.  ...  In addition, a fuzzy neural network is applied to approximate the unmodeled dynamics and a sliding mode controller is developed to enhance the robustness of the system against uncertainties and disturbances  ...  Acknowledgement This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 107.02-2020. 13  ... 
doi:10.1016/j.jfranklin.2020.09.011 fatcat:hn6luloadbalfffpvz3y47slqm

Robust Exponential Stability of Uncertain Fuzzy Stochastic Neutral Neural Networks with Mixed Time-Varying Delays

Yajun Li, Feiqi Deng, Fei Xie, Like Jiao
2018 International Journal of Innovative Computing, Information and Control  
The robust exponential stability problem for a class of uncertain fuzzy stochastic neutral neural networks systems with mixed delays is concerned about.  ...  Based on the Lyapunov functional and the stochastic stability theory, the sufficient conditions are developed in terms of linear matrix inequalities (LMIs).  ...  The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.  ... 
doi:10.24507/ijicic.14.02.615 fatcat:6kcbxaevkbhxvcnjmu7ldyj3em

Predicting potential of controlled blasting-induced liquefaction using neural networks and neuro -fuzzy system

Fariba Asvar, Arash Shirmohammadi Faradonbeh, Kazem Barkhordari
2017 Scientia Iranica. International Journal of Science and Technology  
Next, a neuro-fuzzy network, ANFIS, was used for modeling. Di erent ANFIS models are created using Grid Partitioning (GP), subtractive clustering (SCM), and Fuzzy C-Means clustering (FCM).  ...  Finally, sensitivity analysis for RBF network is tested, and its results reveal that 0 v0 and SPT are the most e ective factors for determining Ru.  ...  Under these conditions, statistical methods and AI-based methods (arti cial neural networks and fuzzy systems) with available data have opened up a new world for researchers.  ... 
doi:10.24200/sci.2017.4184 fatcat:mqv3ilzlabeq3gubr2irkbgrcu
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