Relevant bibliographies by topics / Neural controller

Contents

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

Academic literature on the topic 'Neural controller'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Neural controller"

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Karam, Ekhlas H., Ayam M. Abbass, and Noor S. Abdul-Jaleel. "Design of Hybrid Neural Fuzzy Controller for Human Robotic Leg System." Al-Khwarizmi Engineering Journal 14, no. 1 (2018): 145–55. http://dx.doi.org/10.22153/https://doi.org/10.22153/kej.2018.08.007.

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In this paper, the human robotic leg which can be represented mathematically by single input-single output (SISO) nonlinear differential model with one degree of freedom, is analyzed and then a simple hybrid neural fuzzy controller is designed to improve the performance of this human robotic leg model. This controller consists from SISO fuzzy proportional derivative (FPD) controller with nine rules summing with single node neural integral derivative (NID) controller with nonlinear function. The Matlab simulation results for nonlinear robotic leg model with the suggested controller showed that
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Karam, Ekhlas H., Ayam M. Abbass, and Noor S. Abdul-Jaleel. "Design of Hybrid Neural Fuzzy Controller for Human Robotic Leg System." Al-Khwarizmi Engineering Journal 14, no. 1 (2018): 145–55. http://dx.doi.org/10.22153/kej.2018.08.007.

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In this paper, the human robotic leg which can be represented mathematically by single input-single output (SISO) nonlinear differential model with one degree of freedom, is analyzed and then a simple hybrid neural fuzzy controller is designed to improve the performance of this human robotic leg model. This controller consists from SISO fuzzy proportional derivative (FPD) controller with nine rules summing with single node neural integral derivative (NID) controller with nonlinear function. The Matlab simulation results for nonlinear robotic leg model with the suggested controller showed that
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Passold, Fernando. "Applying RBF Neural Nets for Position Control of an Inter/Scara Robot." International Journal of Computers Communications & Control 4, no. 2 (2009): 148. http://dx.doi.org/10.15837/ijccc.2009.2.2422.

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This paper describes experimental results applying artificial neural networks to perform the position control of a real scara manipulator robot. The general control strategy consists of a neural controller that operates in parallel with a conventional controller based on the feedback error learning architecture. The main advantage of this architecture is that it does not require any modification of the previous conventional controller algorithm. MLP and RBF neural networks trained on-line have been used, without requiring any previous knowledge about the system to be controlled. These approach
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Jiang, Yiming, Chenguang Yang, Min Wang, Ning Wang, and Xiaofeng Liu. "Bioinspired control design using cerebellar model articulation controller network for omnidirectional mobile robots." Advances in Mechanical Engineering 10, no. 8 (2018): 168781401879434. http://dx.doi.org/10.1177/1687814018794349.

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As a learning mechanism that emulates the structure of the cerebellum, cerebellar model articulation controllers have been widely adopted in the control of robotic systems because of the fast learning ability and simple computational structure. In this article, a cerebellar model articulation controller–based neural network controller is developed for an omnidirectional mobile robot. With the powerful learning ability of cerebellar model articulation controller, a cerebellar model articulation controller neural network is constructed to learn the complex dynamics of the omnidirectional mobile
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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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Jung, Seul, and T. C. Hsia. "Neural network inverse control techniques for PD controlled robot manipulator." Robotica 18, no. 3 (2000): 305–14. http://dx.doi.org/10.1017/s0263574799002064.

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In this paper neural network (NN) control techniques for non-model based PD controlled robot manipulators are proposed. The main difference between the proposed technique and the existing feedback error learning (FEL) technique is that compensation of robot dynamics uncertainties is done outside the control loop by modifying the desired input trajectory. By using different NN training signals, two NN control schemes are developed. One is comparable to that in the FEL technique and another has to deal with the Jacobian of the PD controlled robot dynamic system. Performances of both controllers
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Elgargani, T. N., A. A. Hudoud, and S. O. Abid. "IMPROVEMENT AND CONTROL OF THE SPEED RESPONSE OF THE PERMANENT MAGNET SYNCHRONOUS MOTOR DRIVE USING A FUZZY – PI CONTROLLER." Journal of Science and Technology 30, no. 5 (2025): 25–36. https://doi.org/10.20428/jst.v30i5.2812.

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High-speed and high-performance electric motors are designed to reach a high level of demand control. The permanent magnet synchronous motors (PMSMs) drive has a non-linear model that is not easy to deal with using traditional control methods when controlling the three phase motors because of their nature, (intricate highly non-linear model). Therefore, neural networks controllers compared with fuzzy logic controllers (FLCs) are getting more attention among researchers, as they can be used for such systems. The neural networks controller relies on training of this mathematical model, and the f
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Zada, Fatma, Shawket K. Guirguis, and Walied M. Sead. "Hybrid Neural-Fuzzy Controller for Motorized Robot Arm." Advanced Materials Research 403-408 (November 2011): 5068–75. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.5068.

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In this study, a design methodology is introduced that blends the neural and fuzzy logic controllers in an intelligent way developing a new intelligent hybrid controller. In this design methodology, the fuzzy logic controller works in parallel with the neural controller and adjusting the output of the neural controller. The performance of our proposed controller is demonstrated on a motorized robot arm with disturbances. The simulation results shows that the new hybrid neural -fuzzy controller provides better system response in terms of transient and steady-state performance when compared to n
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Nguyen, Tat-Bao-Thien, Teh-Lu Liao, Hang-Hong Kuo, and Jun-Juh Yan. "An Improved Adaptive Tracking Controller of Permanent Magnet Synchronous Motor." Abstract and Applied Analysis 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/987308.

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This paper proposes a new adaptive fuzzy neural control to suppress chaos and also to achieve the speed tracking control in a permanent magnet synchronous motor (PMSM) drive system with unknown parameters and uncertainties. The control scheme consists of fuzzy neural and compensatory controllers. The fuzzy neural controller with online parameter tuning is used to estimate the unknown nonlinear models and construct linearization feedback control law, while the compensatory controller is employed to attenuate the estimation error effects of the fuzzy neural network and ensure the robustness of t
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Dissertations / Theses on the topic "Neural controller"

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Ronco, Eric. "Incremental polynomial controller networks two self-organising non-linear controllers /." Thesis, Connect to electronic version, 1997. http://hdl.handle.net/1905/181.

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Dale, Eirik Skjeggestad. "Neural Net Controller for a Snake Robot." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-26757.

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When we’re talking about the field of robotics, one of the key components is the controller for the robots. The focus has recently begun shifting away from the traditional wheeled robots, into biologically inspired robots. Research is being done on walker robots with different numbers of legs, and also serpentile robots are being worked on. The previous and reasonably well-established robot controllers for the wheeled robots is generally not applicable for the new robots, since it is now a much larger focus on the motorics and how the robot must manipulate its limbs to be able to move. Th
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Patkar, Abhishek. "Adaptive neural controller based on convex parametrization." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128972.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, September, 2020<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 65-67).<br>The problem of control of a class of nonlinear plants has been addressed by using neural networks together with sliding mode control to lead to global boundedness. We revisit this problem in this thesis and suggest a specific class of neural networks that employ convex activation functions. By using the algorithms that have been proposed previously for adaptive control in th
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Sagiroglu, Serkan. "Adaptive Neural Network Applications On Missile Controller Design." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611106/index.pdf.

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In this thesis, adaptive neural network controllers are designed for a high subsonic cruise missile. Two autopilot designs are included in the study using adaptive neural networks, namely an altitude hold autopilot designed for the longitudinal channel and a directional autopilot designed for heading control. Aerodynamic coefficients are obtained using missile geometry<br>a 5-Degree of Freedom (5-DOF) simulation model is obtained, and linearized at a single trim condition. An inverted model is used in the controller. Adaptive Neural Network (ANN) controllers namely, model inversion controllers
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Tan, Xiaodong. "High dimentional neural fuzzy controller for nonlinear systems." Mémoire, Université de Sherbrooke, 2007. http://savoirs.usherbrooke.ca/handle/11143/1470.

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De nos jours, la théorie de contrôle joue un rôle significatif dans presque tous les domaine de la science et de l'ingénierie. Les contrôleurs linéaires PID sont les applications principales de la théorie de contrôle, et ils se basent sur les systèmes de contrôle simples. Mais beaucoup de vrais systèmes possèdent des caractéristiques non-linéaires. Dans la pratique, il est nécessaire de faire beaucoup de linéarisations. Quand nous employons le contrôleur classique dans un système non-linéaire fortement complexe, les difficultés augmentent exponentiellement. Pour éviter les imperfections, on pe
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Haynes, Barry P. "A neural network adaptive controller for non-linear systems." Thesis, University of Portsmouth, 1997. https://researchportal.port.ac.uk/portal/en/theses/a-neural-network-adaptive-controller-for-nonlinear-systems(19584462-246e-4de3-9e80-cda4923a38c1).html.

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Abdulaziz, Ali Ahmed. "Neural-based controller development for solving non-linear control problems." Thesis, University of Newcastle Upon Tyne, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260976.

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Al-Araji, Ahmed. "Design of a cognitive neural predictive controller for mobile robot." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7068.

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In this thesis, a cognitive neural predictive controller system has been designed to guide a nonholonomic wheeled mobile robot during continuous and non-continuous trajectory tracking and to navigate through static obstacles with collision-free and minimum tracking error. The structure of the controller consists of two layers; the first layer is a neural network system that controls the mobile robot actuators in order to track a desired path. The second layer of the controller is cognitive layer that collects information from the environment and plans the optimal path. In addition to this, it
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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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Graf, Daryl H. (Daryl Herbert) Carleton University Dissertation Computer Science. "A neural controller for collision-free movement of robot manipulators." Ottawa, 1988.

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Books on the topic "Neural controller"

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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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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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Jorgensen, Charles C. Development of a sensor coordinated kinematic model for neural network controller training. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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Dinu, Andrei. FPGA neural controller for three-phase sensorless induction motor drive systems. De Montfort University, 2000.

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United States. National Aeronautics and Space Administration., ed. Knowledge-based aircraft automation: Managers guide on the use of artificial intelligence for aircraft automation and verification and validation approach for a neural-based flight controller. National Aeronautics and Space Administration, 1997.

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United States. National Aeronautics and Space Administration., ed. Knowledge-based aircraft automation: Managers guide on the use of artificial intelligence for aircraft automation and verification and validation approach for a neural-based flight controller. National Aeronautics and Space Administration, 1997.

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United States. National Aeronautics and Space Administration., ed. Knowledge-based aircraft automation: Managers guide on the use of artificial intelligence for aircraft automation and verification and validation approach for a neural-based flight controller. National Aeronautics and Space Administration, 1997.

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United States. National Aeronautics and Space Administration., ed. Knowledge-based aircraft automation: Managers guide on the use of artificial intelligence for aircraft automation and verification and validation approach for a neural-based flight controller. National Aeronautics and Space Administration, 1997.

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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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Fowler, Kim. Computer controlled neurological stimulation system: Hardware and firmware descriptions. Johns Hopkins University/Applied Physics Laboratory, 1991.

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Book chapters on the topic "Neural controller"

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Mehmood, Usama, Shouvik Roy, Radu Grosu, Scott A. Smolka, Scott D. Stoller, and Ashish Tiwari. "Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_1.

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AbstractWe show how a symmetric and fully distributed flocking controller can be synthesized using Deep Learning from a centralized flocking controller. Our approach is based on Supervised Learning, with the centralized controller providing the training data, in the form of trajectories of state-action pairs. We use Model Predictive Control (MPC) for the centralized controller, an approach that we have successfully demonstrated on flocking problems. MPC-based flocking controllers are high-performing but also computationally expensive. By learning a symmetric and distributed neural flocking con
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Saerens, Marco, Alain Soquet, Jean-Michel Renders, and Hugues Bersini. "Some Preliminary Comparisons Between a Neural Adaptive Controller and a Model Reference Adaptive Controller." In Neural Networks in Robotics. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3180-7_8.

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Pham, Duc Truong, and Xing Liu. "Neuromorphic Fuzzy Controller Design." In Neural Networks for Identification, Prediction and Control. Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3244-8_7.

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Elgawi, Osman Hassab. "RL-Based Memory Controller for Scalable Autonomous Systems." In Neural Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10684-2_10.

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Liu, Xiaoming, Yulin Tian, Chunlin Shang, Peizhou Yan, and Lu Wei. "Design of Traffic Signal Controller Based on Network." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70139-4_65.

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Moringen, Alexandra, Sascha Fleer, and Helge Ritter. "Scaffolding Haptic Attention with Controller Gating." In Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30487-4_51.

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Perez-Peña, Fernando, Arturo Morgado-Estevez, Alejandro Linares-Barranco, Manuel Jesus Dominguez-Morales, and Angel Jimenez-Fernandez. "SVITE: A Spike-Based VITE Neuro-Inspired Robot Controller." In Neural Information Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42054-2_35.

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Sui, Jianghua, and Guang Ren. "An AND-OR Fuzzy Neural Network Ship Controller Design." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893295_68.

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Enderle, John D., and Wei Zhou. "Neural Network for the Saccade Controller." In Models of Horizontal Eye Movements, Part II. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01643-1_2.

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Rashid, Usman, Mohsin Jamil, Syed Omer Gilani, and Imran Khan Niazi. "LQR Based Training of Adaptive Neuro-Fuzzy Controller." In Advances in Neural Networks. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33747-0_31.

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Conference papers on the topic "Neural controller"

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Cui, Lin, Jianshan Zhou, Mingqian Wang, Zixuan Xu, Xuting Duan, and Chenghao Ren. "Neural Network Controller for Drones." In 2024 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2024. https://doi.org/10.1109/icus61736.2024.10839951.

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Yang, Huayong, Jiangang Yang, Jianmin Zhang, and Daohang Sha. "A Real Time Adaptive Control Algorithm for a VVVF Hydraulic Elevator Using Neural Nets." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-1274.

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Abstract An adaptive algorithm of neural nets with a special perturbation for a real time velocity control system of a VVVF hydraulic elevator has been proposed in this paper. The weight vector of neural network is adoptively adjusted by the LMS with the perturbation, so it is not necessary to know the nonlinear continuous function of the system controlled. The nonlinear velocity control system is considered as the controller output function in an adaptive controller model. The experimental results obtained from the VVVF hydraulic elevator have shown that the neural nets controller using the p
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Kumar, Manish, and Devendra P. Garg. "Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59589.

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Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have
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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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Webb, Rod. "A Neural Switch Controller." In Optics in Computing. Optica Publishing Group, 1997. http://dx.doi.org/10.1364/oc.1997.owb.4.

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In a system with many variables, finding their optimum values is, in general, a computationally intensive process that scales badly with the number of variables. The potential benefit of accelerating the solution of such problems with optical computing techniques is therefore large. Considerable success has been reported [1] with the use of Hopfield neural networks for optimisation and neural network architectures lend themselves to parallel operation in hardware. Optics is appropriate for implementing the large number of connections required, which it can provide very efficiently when there i
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Frenger, P. "A neural net controller." In the first annual workshop. ACM Press, 1989. http://dx.doi.org/10.1145/73312.73319.

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Batayneh, Wafa, and Nash’at Nawafleh. "Comparative Study of DC Motor Speed Control Using Neural Networks and Fuzzy Logic Controller." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51362.

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This paper demonstrates the importance of the intelligent controllers over the conventional methods. A speed control of the DC motor is developed using both Neural Networks and Fuzzy logic controller in MATLAB environment as intelligent controllers. In addition a conventional PID controller is developed for comparison purposes. Both intelligent controllers are designed based on the simulation results of the nonlinear equations in addition to the expert pre knowledge of the system. The output response of the system is obtained using the two types of the intelligent controllers, in addition to t
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Hoptroff, R. G., T. J. Hall, and R. E. Burge. "Experiments with a neural controller." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137788.

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Gupta, M. M., D. H. Rao, and H. C. Wood. "Learning and adaptive neural controller." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170744.

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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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Reports on the topic "Neural controller"

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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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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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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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Kirichek, Galina, Vladyslav Harkusha, Artur Timenko, and Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3743.

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In this article realization method of attacks and anomalies detection with the use of training of ordinary and attacking packages, respectively. The method that was used to teach an attack on is a combination of an uncontrollable and controlled neural network. In an uncontrolled network, attacks are classified in smaller categories, taking into account their features and using the self- organized map. To manage clusters, a neural network based on back-propagation method used. We use PyBrain as the main framework for designing, developing and learning perceptron data. This framework has a suffi
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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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Griffey, Richard. Controlled Enhancemnt of Long-Term Memory by Modulating Neuronal miRNA Function. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada577054.

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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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O'Brien, Beth A., Chee Soon Tan, and Luca Onnis. Technology-based tools for teaching early literacy skills. National Institute of Education, Nanyang Technological University, Singapore, 2024. https://doi.org/10.32658/10497/27453.

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Abstract:
This project focuses on improving literacy development for young learners who are struggling with learning to read English by investigating the process of learning grapheme-phoneme correspondences (GPCs). Learning GPC is foundational to learning to read alphabetic languages, and is a core problem for struggling readers. In this project, two methods are used in two studies to understand the process of learning English GPCs as the crux of acquiring literacy. First, a machine learning neural network modelling approach is used to study the effect of sound-symbol grain size and consistency and trai
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Blinov, D. O., A. I. Fomin, and A. A. Chibin. Neural network model for determining the values of the indicator of the effectiveness of the impact of controlled means on air objects. OFERNiO, 2021. http://dx.doi.org/10.12731/ofernio.2021.24804.

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