Relevant bibliographies by topics / Artificial neural network controller

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Artificial neural network controller'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Artificial neural network controller"

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Karunasena, G. M. K. B., H. D. N. S. Priyankara, and B. G. D. A. Madhusank. "Artificial Neural Network vs PID Controller for Magnetic Levitation System." International Journal of Innovative Science and Research Technology 5, no. 7 (2020): 505–11. http://dx.doi.org/10.38124/ijisrt20jul432.

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This research investigates the acceptability of the Artificial Neural Networks (ANN) over the PID Controller for the control of the Magnetic Levitation System (MLS). In the field of advanced control systems, this system identifies as a feedback-controlled, single input- single output (SISO) system. This SISO system used a PID controller for vertical trajectory controlling of a metal sphere in airspace by using an electromagnetic force that directed to upward. The vertical position of the metal sphere controlled according to the applied magnetic force generated by a powerful electromagnet and t
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Joshi, Girisha, and Pinto Pius A J. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (2020): 1177. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.

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For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis
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Girisha, Joshi, and Pius A. J. Pinto. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 3 (2020): 1177–85. https://doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.

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For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis
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GÖKÇE, Celal Onur. "Intelligent Quadcopter Control Using Artificial Neural Networks." Afyon Kocatepe University Journal of Sciences and Engineering 23, no. 1 (2023): 138–42. http://dx.doi.org/10.35414/akufemubid.1229424.

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An advanced controller architecture and design for quadcopter control implementation is proposed in this study. Instead of using only the error information as input to the controller, reference and measured outputs are used separately independent from each other. This enhances the performance of the controller of quadcopter being a highly non-linear platform. In this study single layer neural network is directly used as a controller. A complex controller is grown from an initially simple PID controller. This elevates the need for time consuming search in huge parameter space due to very high d
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Katada, Yoshiaki, Takumi Hirokawa, Motoaki Hiraga, and Kazuhiro Ohkura. "MBEANN for Robotic Swarm Controller Design and the Behavior Analysis for Cooperative Transport." Journal of Robotics and Mechatronics 35, no. 4 (2023): 997–1006. http://dx.doi.org/10.20965/jrm.2023.p0997.

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This study focuses on mutation-based evolving artificial neural network (MBEANN), a topology and weight evolving artificial neural network (TWEANN) algorithm. TWEANN optimizes both the connection weights and neural network structure. Primarily, MBEANN uses only mutations to evolve artificial neural networks. An individual in an MBEANN is designed to have a set of sub-networks called operons. Operons are expected to have functions during evolution because they do not recombine with other operons. In this study, we applied MBEANN to design a controller for a robotic swarm on cooperative transpor
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K.J., Rathi, and S. Ali M. "Neural Network Controller for Power Electronics Circuits." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 2 (2017): 49–55. https://doi.org/10.5281/zenodo.4108248.

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Artificial Intelligence (AI) techniques, particularly the neural networks, are recently having significant impact on power electronics. This paper explores the perspective of neural network applications in the intelligent control for power electronics circuits. The Neural Network Controller (NNC) is designed to track the output voltage and to improve the performance of power electronics circuits. The controller is designed and simulated using MATLAB-SIMULINK
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Scott, Gary M., Jude W. Shavlik, and W. Harmon Ray. "Refining PID Controllers Using Neural Networks." Neural Computation 4, no. 5 (1992): 746–57. http://dx.doi.org/10.1162/neco.1992.4.5.746.

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The KBANN (Knowledge-Based Artificial Neural Networks) approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. We extend this idea further by presenting the MANNCON (Multivariable Artificial Neural Network Control) algorithm by which the mathematical equations governing a PID (Proportional-Integral-Derivative) controller determine the topology and initial weights of a network, which is further trained using backpropagation. We apply this method to the task of controlling the outflow and temperature of a water tank, producing statisticall
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Obed, Adel, and Ameer Saleh. "Speed Control of BLDC Motor Based on Recurrent Wavelet Neural Network." Iraqi Journal for Electrical and Electronic Engineering 10, no. 2 (2014): 118–29. http://dx.doi.org/10.37917/ijeee.10.2.7.

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In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learnin
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Somwong, Poom, Karn Patanukhom, and Yuthapong Somchit. "Energy-Aware Controller Load Distribution in Software-Defined Networking using Unsupervised Artificial Neural Networks." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 16, no. 1 (2025): 289–314. https://doi.org/10.58346/jowua.2025.i1.018.

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Software-Defined Networking (SDN) enhances network management by separating the control and data planes into controllers and switches, allowing for centralized, programmable networks with multiple controllers. Switches are mapped to controllers and exchange control messages to manage the network, which leads to significant energy consumption. Managing energy in networks has become a critical issue, as dynamic changes in switch loads can cause controller overloads, necessitating the migration of switches to other controllers. As networks grow, energy consumed in control communications becomes a
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Woodford, Grant W., and Mathys C. du Plessis. "Complex Morphology Neural Network Simulation in Evolutionary Robotics." Robotica 38, no. 5 (2019): 886–902. http://dx.doi.org/10.1017/s0263574719001140.

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SUMMARYThis paper investigates artificial neural network (ANN)-based simulators as an alternative to physics-based approaches for evolving controllers in simulation for a complex snake-like robot. Prior research has been limited to robots or controllers that are relatively simple. Benchmarks are performed in order to identify effective simulator topologies. Additionally, various controller evolution strategies are proposed, investigated and compared. Using ANN-based simulators for controller fitness estimation during controller evolution is demonstrated to be a viable approach for the high-dim
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Dissertations / Theses on the topic "Artificial neural network controller"

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BRUCE, WILLIAM, and OTTER EDVIN VON. "Artificial Neural Network Autonomous Vehicle : Artificial Neural Network controlled vehicle." Thesis, KTH, Maskinkonstruktion (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191192.

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This thesis aims to explain how a Artificial Neural Network algorithm could be used as means of control for a Autonomous Vehicle. It describes the theory behind the neural network and Autonomous Vehicles, and how a prototype with a camera as its only input can be designed to test and evaluate the algorithms capabilites, and also drive using it. The thesis will show that the Artificial Neural Network can, with a image resolution of 100 × 100 and a training set with 900 images, makes decisions with a 0.78 confidence level.<br>Denna rapport har som mal att beskriva hur en Artificiellt Neuronnatve
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Karakasoglu, Ahmet. "Neural network-based approaches to controller design for robot manipulators." Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185612.

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This dissertation is concerned with the development of neural network-based methods to the control of robot manipulators and focusses on three different approaches for this purpose. In the first approach, an implementation of an intelligent adaptive control strategy in the execution of complex trajectory tracking tasks by using multilayer neural networks is demonstrated by exploiting the pattern classification capability of these nets. The network training is provided by a rule-based controller which is programmed to switch an appropriate adaptive control algorithm for each component type of m
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Ariss, Joseph, and Salim Rabat. "A comparison between a traditional PID controller and an Artificial Neural Network controller in manipulating a robotic arm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259365.

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Robotic and control industry implements different control technique to control the movement and the position of a robotic arm. PID controllers are the most used controllers in the robotics and control industry because of its simplicity and easy implementation. However, PIDs’ performance suffers under noisy environments. In this research, a controller based on Artificial Neural Networks (ANN) called the model reference controller is examined to replace traditional PID controllers to control the position of a robotic arm in a noisy environment. Simulations and implementations of both controllers
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Conlon, Martin J. "Design and evaluation of a neural network-based controller for an artificial heart." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ57723.pdf.

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Conlon, Martin J. (Martin John) Carleton University Dissertation Engineering Mechanical and Aerospace. "Design and evaluation of a neural network-based controller for an artificial heart." Ottawa, 2000.

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Narayanan, Pavanesh. "Sensor-less Control of Shape Memory Alloy Using Artificial Neural Network and Variable Structure Controller." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1416501021.

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Sharma, Anuj. "Determination of traffic responsive plan selection factors and thresholds using artificial neural networks." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1228.

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Traffic congestion has become a menace to civilized society. It degrades air quality, jeopardizes safety and causes delay. Traffic congestion can be alleviated by providing an effective traffic control signal system. Closed-loop traffic control systems are an example of such a system. Closed-loop traffic control systems can be operated primarily in either of two modes: Time of Day Mode (TOD) or Traffic Responsive Plan Selection Mode (TRPS). TRPS mode, if properly configured, can easily handle time independent variation in traffic volumes. It can also reduce the effect of timing plan aging. De
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Chamanirad, Mohsen. "Design and implementation of controller for robotic manipulators using Artificial Neural Networks." Thesis, Mälardalen University, School of Innovation, Design and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-6297.

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<p>In this thesis a novel method for controlling a manipulator with arbitrary number of Degrees of freedom is proposed, the proposed method has the main advantages of two common controllers, the simplicity of PID controller and the robustness and accuracy of adaptive controller. The controller architecture is based on an Artificial Neural Network (ANN) and a PID controller.</p><p>The controller has the ability of solving inverse dynamics and inverse kinematics of robot with two separate Artificial Neural Networks. Since the ANN is learning the system parameters by itself the structure of contr
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Hutt, Benjamin David. "Evolving artificial neural network controllers for robots using species-based methods." Thesis, University of Reading, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270831.

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Cheng, Chao. "Application of Artificial Neural Networks in the Power Split Controller For a Series Hydraulic Hybrid Vehicle." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1278610645.

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Books on the topic "Artificial neural network controller"

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Denise, Taylor Lynore, and United States. National Aeronautics and Space Administration., eds. Artificial neural network implementation of a near-ideal error prediction controller. Dept. of Electrical Engineering, School of Engineering and Applied Science, University of Virginia, 1992.

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Shanmuganathan, Subana, and Sandhya Samarasinghe, eds. Artificial Neural Network Modelling. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8.

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S, Mohan. Artificial neural network modelling. Indian National Committee on Hydrology, 2007.

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Jorgensen, Charles C. Distributed memory approaches for robotic neural controllers. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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Rylatt, R. Mark. Investigations into controllers for adaptive autonomous agents based on artificial neural networks. De Montfort University, 2001.

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Haynes, B. P. A neural network adaptive controller for non-linear systems. University of Portsmouth, Faculty of Technology, 1997.

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Bisi, Manjubala, and Neeraj Kumar Goyal. Artificial Neural Network for Software Reliability Prediction. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119223931.

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Roberts, S. G. The evolution of artificial neural network structures. UMIST, 1997.

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Kattan, Ali. Artificial neural network training and software implementation techniques. Nova Science Publishers, 2011.

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Kattan, Ali. Artificial neural network training and software implementation techniques. Nova Science Publishers, 2011.

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Book chapters on the topic "Artificial neural network controller"

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Zhao, Yibiao, Rui Fang, Shun Zhang, and Siwei Luo. "Vague Neural Network Controller and Its Applications." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_83.

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Liu, Shaohua, Hongkun Dou, Hongjue Li, and Yue Deng. "An Optical Satellite Controller Based on Diffractive Deep Neural Network." In Artificial Intelligence. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20500-2_4.

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Yang, Jianhua, Wei Lu, and Wenqi Liu. "PID Controller Based on the Artificial Neural Network." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28648-6_22.

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Thierens, Dirk, Johan Suykens, Joos Vandewalle, and Bart De Moor. "Genetic Weight Optimization of a Feedforward Neural Network Controller." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-7533-0_95.

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Mohan, Vishwanathan, and Pietro Morasso. "A Forward / Inverse Motor Controller for Cognitive Robotics." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_63.

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Drchal, Jan, Ondrej Kapral, Jan Koutník, and Miroslav Šnorek. "Combining Multiple Inputs in HyperNEAT Mobile Agent Controller." In Artificial Neural Networks – ICANN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_78.

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Benchikh, Salma, Tarik Jarou, Mohamed Khalifa Boutahir, Elmehdi Nasri, and Roa Lamrani. "Design of Artificial Neural Network Controller for Photovoltaic System." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-48573-2_81.

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Mohammed, Benzaouia, Hajji Bekkay, Rabhi Abdelhamid, and Benzaouia Soufyane. "Experimental Assessment of MPPT Based on a Neural Network Controller." In Artificial Intelligence and Smart Environment. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26254-8_58.

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Gökbulut, Muammer, Beşir Dandil, and Cafer Bal. "A Hybrid Neuro-Fuzzy Controller for Brushless DC Motors." In Artificial Intelligence and Neural Networks. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11803089_15.

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Ribeiro, B., and A. Cardoso. "A Model-based Neural Network Controller for a Process Trainer Laboratory Equipment." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_133.

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Conference papers on the topic "Artificial neural network controller"

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Sravani, D. L. N., and K. H. Phani Shree. "Artificial Neural Network Controller for Doubly Fed Induction Generator-Based Wind Energy Conversion System." In 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET). IEEE, 2024. http://dx.doi.org/10.1109/sefet61574.2024.10718073.

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Pal, Kumaresh, Ashok Kumar Akella, Kumari Namrata, and Subhendu Pati. "Face Detection Using Artificial Neural Network and Wavelet Neural Network." In 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP). IEEE, 2022. http://dx.doi.org/10.1109/iciccsp53532.2022.9862349.

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Kudlacak, Frantisek, and Tibor Krajcovic. "Artificial neural network for adaptive PID controller." In 2018 Cybernetics & Informatics (K&I). IEEE, 2018. http://dx.doi.org/10.1109/cyberi.2018.8337564.

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Majors, M. D. "A neural-network-based flexible assembly controller." In 4th International Conference on Artificial Neural Networks. IEE, 1995. http://dx.doi.org/10.1049/cp:19950566.

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Veloz, Alejandro, Juan C. Romero Quintini, Mónica Parada, and Sergio E. Diaz. "Experimental Testing of a Magnetically Levitated Rotor With a Neural Network Controller." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-69120.

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Magnetic bearings represent a solution for high rotating speeds and sterile environments where lubrication fluids could contaminate. They can also be used in systems where maintenance is difficult or inaccessible, because they don’t require auxiliary lubrication systems and don’t suffer mechanic wear as they work with no contact between rotor and bearing stator. An important part of magnetic bearings is the controller; which is needed to stabilize the system. This controller is generally a PID in which tuning and/or filters design can be complicated for not well known systems. This work presen
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Khan, Mohammad Ali, Prashant Anand, and G. Bhuvaneswari. "Artificial Neural Network based controller design for SMPS." In 2019 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE). IEEE, 2019. http://dx.doi.org/10.1109/rdcape47089.2019.8979104.

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Lftisi, F., G. H. George, A. Aktaibi, C. B. Butt, and M. A. Rahman. "Artificial neural network based speed controller for induction motors." In IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2016. http://dx.doi.org/10.1109/iecon.2016.7793117.

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Dahhou, Brahim, and Ahmed Bouraiou. "Speed Control Of DFIM Using Artificial Neural Network Controller." In 2022 2nd International Conference on Advanced Electrical Engineering (ICAEE). IEEE, 2022. http://dx.doi.org/10.1109/icaee53772.2022.9961983.

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Mohammad, Kasim, and Sarhan M. Musa. "Optimization of Solar Energy Using Artificial Neural Network Controller." In 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2022. http://dx.doi.org/10.1109/cicn56167.2022.10008271.

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Mohammad, Kasim, and Sarhan M. Musa. "Optimization of Solar Energy Using Artificial Neural Network Controller." In 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2022. http://dx.doi.org/10.1109/cicn56167.2022.10008359.

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Reports on the topic "Artificial neural network controller"

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Vitela, J. E., U. R. Hanebutte, and J. Reifman. An artificial neural network controller for intelligent transportation systems applications. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/219376.

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Russell, Chris A., and Glenn F. Wilson. Application of Artificial Neural Networks for Air Traffic Controller Functional State Classification. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada404631.

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Powell, Bruce C. Artificial Neural Network Analysis System. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada392390.

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Braganza, D., D. M. Dawson, I. D. Walker, and N. Nath. Neural Network Grasping Controller for Continuum Robots. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada462583.

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Karakowski, Joseph A., and Hai H. Phu. A Fuzzy Hypercube Artificial Neural Network Classifier. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada354805.

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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa
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Patro, S., and W. J. Kolarik. Integrated evolutionary computation neural network quality controller for automated systems. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/350895.

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Sgurev, Vassil. Artificial Neural Networks as a Network Flow with Capacities. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.09.12.

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Vela, Daniel. Forecasting latin-american yield curves: an artificial neural network approach. Banco de la República, 2013. http://dx.doi.org/10.32468/be.761.

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Nikiforov, Vladimir. The use of composite materials in smart medical equipment, including with innovative laser systems, controlled and controlled complexes with elements of artificial intelligence and artificial neural networks. Intellectual Archive, 2019. http://dx.doi.org/10.32370/iaj.2133.

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