Интеллектуальный робастный регулятор на технологиях когнитивных вычислений. Ч. 1: модели когнитивного управления с эмоциональным обучением мозга

Автор: Шевченко Алла Александровна, Шевченко Андрей Владимирович, Тятюшкина Ольга Юрьевна, Ульянов Сергей Викторович

Журнал: Сетевое научное издание «Системный анализ в науке и образовании» @journal-sanse

Статья в выпуске: 4, 2020 года.

Бесплатный доступ

Системах управления и принятия решений в режиме реального времени эмоциональное обучение мозга является более предпочтительной методологией (по сравнению с методами на основе стохастического градиента и эволюционных алгоритмов) из-за своей низкой вычислительной сложности и быстрого робастного обучения. Для описания эмоционального обучения мозга была создана математическая модель - контроллер эмоционального обучения мозга (BELC). Проектирование интеллектуальных систем, основанных на эмоциональных сигналах, строится с применением методов управления на основе технологий мягких вычислений: искусственных нейронных сетей, нечеткого управления и генетических алгоритмов. На основе смоделированной математической модели млекопитающих BEL разработана архитектура контроллера под названием «Интеллектуальный регулятор на основе эмоционального обучения мозга» (англ. BELBIC - Brain Emotional Learning Based Intelligent Controller) - нейробиологически мотивированный интеллектуальный регулятор, основанный на вычислительной модели эмоционального обучения в лимбической системе млекопитающих. В статье описаны модели интеллектуальных регуляторов на основе эмоционального обучения мозга. Возможности обучения, многоцелевые свойства и низкая вычислительная сложность BELBIC делают его перспективным инструментом для применения в приложениях реального времени.

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Когнитивные вычисления, когнитивное управление, эмоциональное управление мозга, когнитивный регулятор

Короткий адрес: https://sciup.org/14123329

IDR: 14123329

Список литературы Интеллектуальный робастный регулятор на технологиях когнитивных вычислений. Ч. 1: модели когнитивного управления с эмоциональным обучением мозга

Kondapaneni, N. Number sense as an emergent property of the manipulating brain / N. Kondapaneni, P. Perona // arXiv preprint. – 2020. – arXiv:2012.04132.
Restle, F. Speed of adding and comparing numbers // Journal of Experimental Psychology. – 1970. – Vol. 83. – № 2. – P. 274.
Dehaene, S. The mental representation of parity and number magnitude / S. Dehaene, S. Bossini, P. Gi-raux.// Journal of Experimental Psychology. – 1993. – Vol. 122. – № 3. – P. 371.
Arithmetic and the brain / S. Dehaene, N. Molko, L. Cohen, A. Wilson // Current Opinion in Neurobiol-ogy. – 2004. – Vol. 14. – № 2. – Pp. 218–224.
Number space mapping in the new born chick resembles humans’ mental number line / R. Rugani, G. Vallortigara, K. Priftis, L. Regolin // Science. – 2015. – Vol. 347. – № 6221. – Pp. 534–536.
Rajesh, P. Rao Т. Brain Co-Processors: Using AI to restore and augment brain function // arXiv preprint. – 2020. – https://arxiv.org/abs/2012.03378.
Task-specific somatosensory feedback via cortical stimulation in humans / J. Cronin, J. Wu, K. Collins, D. Sarma, R. Rao, J. Ojemann, J. Olson. // IEEE Trans Haptics. – 2016. – Vol. 9. – № 4. – Pp. 515–522.
Direct stimulation of somatosensory cortex results in slower reaction times compared to peripheral touch in humans / D. Caldwell, J. Cronin, J. Wu, K. Weaver, A. Ko, R. Rao, J Ojemann // Nature Sci Rep. – 2019. – Vol. 9. – № 1. – P. 3292.
Kalman, R. A. New Approach to Linear Filtering and Prediction Problems // Journal of Basic Engineering. – 1960. – Vol. 82. – Pp. 35–45.
Bryson, A. Applied Optimal Control / A. Bryson, Y. Ho // Halsted Press, 1995.
Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays / G. Mena, L. Grosberg, S. Madugula, P. Hottowy, A. Litke, J. Cunningham, E. Chichilnisky, L. Paninski. // PLoS Comput Biol. – 2017. – Vol. 13. – № 11.
O'shea, D. ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings / D. O'shea, K. Shenoy. // J Neural Eng. – 2018. – Vol. 15. – № 2.
Zhou, A. Toward true closed-loop neuromodulation: artifact- free recording during stimulation / A. Zhou, B. Johnson, R. Muller.// Curr Opin Neurobiol. – 2018. – Vol. 50. – Pp. 119–127.
Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning / D. Cald-well, J. Cronin, R. Rao, K. Collins, K. Weaver, A. Ko, J. Ojemann, J. Kutz, B. Brunton // J Neural Eng, 2020. – Vol. 17. – № 2.
Delgado, J. Physical Control of the Mind: Toward a Psychocivilized Society // Harper and Row, 1969.
Cortical Brain-Computer Interface for Closed-Loop Deep Brain Stimulation / J. Herron, M. Thompson, T. Brown, H. Ojemann, A. Ko. // IEEE Trans Neural Syst Rehabil Eng. – 2017. – Vol. 25. – № 11. – Pp. 2180–2187.
Jackson, A. Long-term motor cortex plasticity induced by an electronic neural implant / A. Jackson, J. Mavoori, E. Fetz. // Nature. – 2006. – Vol. 444. – № 7115. – Pp. 56–60.
Restoration of function after brain damage using a neural prosthesis / D. Guggenmos, M. Azin, S. Barbay, J. Mahnken, C. Dunham, P. Mohseni, R. Nudo. // Proc Natl Acad Sci U S A. – 2013. – Vol. 110. – № 52. – Pp. 77–82.
High performance communication by people with paralysis using an intracortical brain-computer inter-face / C. Pandarinath, P. Nuyujukian, C. Blabe, B. Sorice, J. Saab, F. Willett, L. Hochberg, K. Shenoy, J. Henderson. // Elife. – 2017. – Vol. 21. – № 6.
Rao, R. When two brains connect / R. Rao, A. Stocco. // Sci. Am. Mind. – 2014. – Vol. 25. – Pp. 36–39.
Losey, D. Navigating a 2D Virtual World using Direct Brain Stimulation / D. Losey, A. Stocco, J. Aber-nethy, R. Rao. // Frontiers in Robotics and AI, 2016.
Farwell, L. The truth will out: interrogative polygraphy ('lie detection') with event-related brain potentials / L. Farwell, E. Donchin // Psychophysiology. – 1991. – Vol. 28. – № 5. – Pp. 31–47.
Kozel, F. Detecting deception using functional magnetic resonance imaging / F. Kozel, K. Johnson, Q. Mu // Biol Psychiatry. – 2005. – Vol. 58. – № 6. – Pp. 5–13.
Goering, S. On the Necessity of Ethical Guidelines for Novel Neurotechnologies / S. Goering, R. Yuste. // Cell. – 2016. – Vol. 167. – № 4. – Pp. 882–885.
Yuste, R. Four ethical priorities for neurotechnologies and AI / R. Yuste, S. Goering // Nature. – 2017. – Vol. 551. – № 7679. – Pp. 159–163.
A direct brain-to-brain interface in humans / R. Rao, A. Stocco, M. Bryan, D. Sarma, T. Youngquist, J. Wu, C. Prat // PLoS One. – 2014. – Vol. 5. – № 9. – P. 332.
Rao, R. When two brains connect / R. Rao, A. Stocco // Sci. Am. Min. – 2014. – Vol. 25. – Pp. 36–39.
Playing 20 Questions with the Mind: Collaborative Problem Solving by Humans Using a Brain-to-Brain Interface / A. Stocco, C. Prat, D. Losey, J. Wu, J. Abernethy, R. Rao // PLoS One . – 2015. – Vol. 10. – № 9. – P. 303.
Conscious brain-to-brain communication in humans using non-invasive technologies / C. Grau, R. Gin-houx, A. Riera, T. Nguyen, H. Chauvat, M. Berg, J. Amengual, A. Pascualleone, G. Ruffini // PLoS One. – 2014. – Vol. 9. – № 8. – P. 205.
Non-invasive transmission of sensorimotor information in humans using an EEG/focused ultrasound brain-to- brain interface / W. Lee, S. Kim, B. Kim, C. Lee, Y. Chung, L. Kim, S. Yoo // PLoS One. – 2017. – Vol. 12. – № 6. – P. 476.
Rich, M. Plasticity at Thalamo-amygdala Synapses Regulates Co-caine-Cue Memory Formation and Ex-tinction / M. Rich, Y. Huang, M. Torregrossa // Cell Rep. – 2019. – Vol. 26. – № 4. – Pp. 1010–1020.
Breazeal, C. Designing sociable robots (Intelligent robots and autonomous agents) // Cambridge, Mass.: MIT Press, 2002. – Vol. 18. – P. 263.
Dautenhahn, K. The art of designing socially intelligent agents: Science, fiction, and the human in the loop // Applied artificial intelligence. –1998. – Vol. 12. – № 7. – Pp. 573–617.
Pipe, G. Cooperation between Humans and Humanoid Assistive Robots // Biomimetics: Nature-Based Innovation, 2012.
Breazeal, C. Robots that imitate humans / C. Breazeal, B. Scassellati // Trends in cognitive sciences. – 2002. – Vol. 6. – № 11. – Pp. 481–487.
Leite, I. Social Robots for Long-Term Interaction: A Survey / I. Leite, C. Martinho, A. Paiva // Interna-tional Journal of Social Robotics. – 2013. – Vol. 5. – № 2. – Pp. 291–308.
Kuwamura K. Can We Talk through a Robot As if Face-to-Face? Long-Term Fieldwork Using Teleoperated Robot for Seniors with Alzheimer's Disease / K. Kuwamura, S. Nishio, S. Sato // Front Psychol. – 2016. – Vol. 7. – P. 1066.
Abdi, J. Scoping review on the use of socially assistive robot technology in elderly care / J. Abdi, T. Alhindawi, M. Vizcaychipi // BMJ Open. – 2018. – Vol. 8. – № 2.
A Survey of Behavioral Models for Social Robots / L. Nocentini, G. Acerbi, A. Sorrentino, G. Mancioppi, F. Cavallo // Robotics. – 2019. – Vol. 8. – № 54.
Chita-tegmark, M. Assistive Robots for the Social Management of Health: A Framework for Robot De-sign and Human–Robot Interaction Research / M. Chita-tegmark, M. Scheutz // International Journal of Social Robotics. – 2020 – Pp. 1–21.
Exploiting ability for human adaptation to facilitate improved human-robot interaction and acceptance / P. Calebsolly, S. Dogramadzi, C. Huijnen, H. Heuvel // The Information Society. – 2018. – Vol. 34. – № 3. – Pp. 153–165.
Social robots: The influence of human and robot characteristics on acceptance / L. Bishop, A. Maris, S. Dogramadzi, N. Zook // Journal of Behavioral Robotics. – 2019. – Vol. 10. – № 1. – Pp. 346–358.
Hertzfeld, E. Japan’s Henn-na Hotel fires half its robot workforce. Hotel Management // https://www.ho-telmanagement.net/tech/japan-s-henn-na-hotel-fires-half-its-robot-workforce, 2019.
A Systematic Review of Ten Years of Research on Human Interaction with Social Robots // International Journal of Human–Computer Interaction, 2020. – Pp. 1–14.
P. Simoens. Internet of robotic things: Context-aware and personalized interventions of assistive social robots (short paper) / A. Lambert, N. Norouzi, G. Welch, G. Bruder // 5th IEEE International Conference on Cloud Net-working (Cloudnet). – 2016. – Vol. 5. – Pp. 204–207.
Turcu, C. The Social Internet of Things and the RFID-based robots / C. Turcu, C. Turcu. // IV Interna-tional Congress on Ultra-Modern Telecommunications and Control Systems, 2012. – Pp. 77–83.
A. Vulpe. IoT Security Approaches in Social Robots for Ambient Assisted Living Scenarios // 22nd Inter-national Symposium on Wireless Personal Multimedia Communications (WPMC), 2019. – Pp. 1–6.
Multidisciplinary design approach for implementation of interactive services / I. Kuo, E. Jayawardena, B. Broadbent, A. Macdonald. // International Journal of Social Robotics. – 2011. – Vol. 3. – № 4. – Pp. 443-456.
Breazeal, C. Robot in society: Friend or appliance / C. Breazeal, J. Velasquez // Proceedings of the 1999 Autonomous Agents Workshop on Emotion-Based Agent Architectures, 2004. – Pp. 18–26.
Arkin, R. Affect in Human-Robot Interaction / R. Arkin, L. Moshkina // The Oxford handbook of affective computing, 2015. – Pp. 483–493.
Menne, I. Faces of Emotion: Investigating Emotional Facial Expressions To-wards / I. Menne, F. Schwab // International Journal of Social Robots, 2017. – P. 1.
Wang, Y. In our own image? Emotional and neural processing differences / Y. Wang, S. Quadflieg // Social Cognitive and Affective Neuroscience. – 2015. – Vol. 10. – № 11. – Pp. 1515–1524.
Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots / W. Maitreyee, L. Maria, D. Bazo, R. Craig, H. Weissbart, A. Etoundi, T. Reichenbach, P. Iyenger, C. James, P. Barnaghi, C. Melhuish, R. Vaidyanathan // arXiv preprint. – 2020. – https://arxiv.org/abs/2012.04511.
Electrophysiological studies of face perception in humans / S. Bentin, T. Allison, A. Puce, E. Perez, G. Mccarthy. // Journal of Сognitive Neuroscience. – 1996. – Vol. 8. – № 6. – Pp. 551–565.
Nelles, O. Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models // Springer-Verlag, 2001.
Haykin, S. Neural Networks: A Comprehensive Foundation // Upper Saddle River, NJ: Prentice Hall, 2nd ed., 1999.
Jang, R. Neuro-Fuzzy and Soft Computing: A computational approach to Learning and Machine Intelli-gence / R. Jang, C. Sun, E. Mizutani // Upper Saddle River, NJ: Prentice Hall, 1997.
Gomezgil, P. Experiments with a hybrid complex neural networks for long term prediction of electrocar-diograms / P. Gomezgil, M. Ramirezcortes // Proceedings of the IEEE 2006 International World Congress of Computational Intelligence, IJCNN, 2006. – Pp. 4078–4083.
Ledoux, J. The emotional brain: the mysterious underpinnings of emotional life // Simon & Schuster, 1998.
Moren, J. A computational model of emotional learning in the amygdala / J. Moren, C. Balkenius // From Animals to Animats, 2000.
Izeman, A. J. Zurich sunspot relative numbers / A. Izeman, J. Wolf // The Mathematical Intelligence Journal. – 1998. – Vol. 7. – P. 27–33.
Sipper, M. Convergence to Uniformity in A Cellular Automaton via Local Co-evolution / M. Sipper, M. Tomassini // International Journal of Modern Physics. – 1997. – Vol. 8. – № 5. – Pp. 1013–1024.
Lucas, C. Co-evolutionary Approach to Graph-Coloring Problem / C. Lucas, D. Shahmirzadi, M. Biglar-begian // Technical Journal of Amirkabir University of Technology. – 2003. – Vol. 14. – № 54. – Pp. 363–369.
Hofmeyr, S. Architecture for an Artificial Immune System / S. Hofmeyr, S. Forrest // Journal of Evolu-tionary Computation. – 2000. – Vol. 7. – № 1. – Pp. 45–48.
DNA Computing Implementing Genetic Algorithms / J. Chen, E. Antipov, B. Lemieux, W. Cedeno, D. Wood // Workshop on Evolution as Computation, Piscataway, New Jersey, 1999. – Pp. 39–49.
Fatourechi, M. Reducing Control Effort by Means of Emotional Learning / M. Fatourechi, C. Lucas, A. Khakisedigh // Proceedings of 19th Iranian Conference on Electrical Engineering, Tehran, Iran, 2001. – Vol. 41. – Pp. 1–8.
Shahmirzadi, D. Computational Modeling of the Brain Limbic System and its Application In Control Engineering // Master dissertation, Texas A&M University, U.S.A., 2005.
Maren, S. Long-Term Potentiation in the Amygdala: A Mechanism for Emotional Learning and Memory // Trends in Neurosciences. – 1999. – Vol. 22. – № 12. – Pp. 561–567.
Narendra, K. Identification and control of dynamical systems using neural networks / K. Narendra, K. Parthasarathy // IEEE Trans. on Neural Networks. – 1990. – Vol. 1. – № 1. – Pp. 4–27.
Purves, G. Neuroscience / G. Purves, D. Fitzpatrick. // Sinauer Associates, 2001.
Ohman, A. Fears, Phobias, and Preparedness: Toward and Evolved Module of Fear and Fear Learning / A. Ohman, S. Mineka // Journal of Psychological Review. – 2001. – Vol. 108. – № 3. – Pp. 483–522.
Kelly, J. The Neural Basis of Perception and Movement // Principles of Neural Science, London. UK: Prentice Hall, 1991.
Schachter, S. Some Extraordinary Facts About Obese Humans and Rats // American Psychologist, 1970. – Vol. 26. – Pp. 129–144.
Tolman, E. Introduction and Removal of Reward and Maze Performance in Rats / E. Tolman, C. Honzik // California: University of California Publications in Psychology, 1930.
Sadeghieh, A. Implementation of an intelligent adaptive controller for an electrohydravlic servo system based on a brain mechanism of emotional learning / A. Sadeghieh, J. Roshanian, F. Najafari // Intern. J. of Advanced Robotic Systems (INTECH). – 2012. – Vol. 9. – Pp. 1–12.
Fatourechi, M. Reducing Control Effort by means of Emotional Learning / M. Fatourechi, C. Lucas, A. Khakisedigh // Proc. of 9th Iranian Conf. on Electrical Engineering, ICEE’01, Tehran, Iran, 2001. – Vol. 41. – Pp. 1–8.
Perlovsky, L. Emotions, Learning and control // Proc. of IEEE Int. Symp. on Intelligent Control // Intel-ligent systems and semiotics, Cambridge, MA, 1999. – Pp. 132–137.
Ventura, R. Emotion based control systems / R. Ventura, C. Pinto-Ferreira // Proc. of IEEE Int. Symp. on Intelligent control // Intelligent Systems and Semiotics, Cambridge, MA, 1999. – Pp. 64–66.
Inoue, K. On a Decision-Making System with Emotion / K. Inoue, K. Kawabata, H. Kobayashi // Proc. 5th IEEE International Workshop on Robot and Human Communication, 1996. – Pp. 461–465.
Jazbi, A. Intelligent control with emotional Learning / A. Jazbi, C. Lucas // 7th Iranian Conference on Electrical Engineering, ICEE’99, Tehran, Iran, 1999. – Pp. 207–212.
Picard, R. Healey. Toward machine emotional intelligence: Analysis of affective physiological state/ R. Picard, E. Vyzas, J. Healey // IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001. – Vol. 23. – № 10. – Pp. 1175–1191.
Brain emotional learning based intelligent controller applied to neuro-fuzzy model of micro-heat ex-changer / H. Rouhani, M. Jalili, B. Araabi, W. Eppler, C. Lucas // Expert Systems with Applications. – 2007. – Vol. 32. – Pp. 911–924.
Lucas, C. Introducing BELBIC: Brain Emotional Learning Based Intelligent Controller / C. Lucas, D. Shahmirzadi, N. Sheikholeslami // International Journal of Intelligent Automation and Soft Computing. – 2004. – Vol. 10. – № 1. – Pp. 11–22.
Sadeghieh, A. Implementation of an Intelligent Adaptive Controller for an Electrohydraulic Servo System Based on a Brain Mechanism of Emotional Learning / A. Sadeghieh, J. Roshanian, F. Najafi // Interna-tional Journal of Advanced Robotic Systems. – 2012. – Vol. 9. – № 1.
Implementation of Emotional Controller (BELBIC) for Synchronous Reluctance Motor Drive Proc / E. Daryabeigi, A. Zarchi, G. Arab markadeh, M. Rahman // IEEE Intern. Electric Machines & Drivers Conf. (IEMDC), 2011. – Pp. 1066–1093.
Ershadi, M. Comparison of Fuzzy and Brain Emotional Learning Based Intelligent Control approaches for a Full Bridge DC-DC Converter/ M. Ershadi, S. Shojaeian, R. Keramat // Intern. J. of Industrial Elec-tronics, Control and Optimization. – 2019. – Vol. 2. – № 3. – Pp. 197–206.
Dorrah, H. PSO-BELBIC scheme for two-coupled distillation column process / H. Dorrah, A. Elgarhy, M. Elshimy // Journal of Advanced Research. – 2011. – Vol. 2. – № 1. – Pp. 73–83.
Valizadeh, S. A particle-swarm-based approach for optimum design of BELBIC controller in AVR sys-tem / S. Valizadeh, M. Jamali, C. Lucas // International Conference on Control, Automation and Systems (ICCAS), 2008. – Vol. 26. – Pp. 79–84.
A new Lyapunov based algorithm for tuning BELBIC for a group of linear systems / S. Jafarzadeh, M. Motlagh, M. Barkhordari, R. Mirheidari// Proc of 16th Mediterranean Conference on Control and Automation Congress Centre, 2008. – Pp. 593–595.
Garmsiri, N. Fuzzy Tuning of Brain Emotional Learning Based Intelligent Controllers / N. Garmsiri, F. Najafi // Proceedings of the 8th World Congress on Intelligent Control and Automation, 2010. – Pp. 5296–5301.
Jafari, M. Optimal tuning of Brain Emotional Learning Based Intelligent Controller using Clonal Selection Algorithm / M. Jafari, A. Mohammad Shahri, S. Elyas // ICCKE 2013, 2013. – Pp. 30–34.
Lipo, T. Synchronous Reluctance Machines- A viable alternative for ac drives // Electric Ma-chines and Power Systems. – 1991. – Vol. 19. – Pp. 659–671.
Betz, R. Control of Synchronous Reluctance Machies / R. Betz, R. Lagerquist, M. Jovanovic // IEEE Trans. On Industry Application. – 1993. – Vol. 29. – № 6. – Pp. 1110–1122.
Doncker, R. The universal field oriented (UFO) controller in the air gap reference frame / R. Doncker, F. Profumo, A. Tenconi // Inst. Elect. Eng, 1993. – Vol. 13-D. – № 4. – Pp. 477–486.
Vector control of a synchronous reluctance motor including saturation and iron loss / L. Xu, X. Xu, T. Lipo, W. Novotny // IEEE Trans. Ind. Applicat. – 1991. – Vol. 27. – Pp. 977–985.
Park, J. Control of high-speed solid- rotor synchronous reluctance motor/generator for fly wheel-based uninterruptible power supplies / J. Park, C. Kalev, H. Hofmann // IEEE Trans. Ind. Electron. – 2008. – Vol. 55. – № 8. – Pp. 3038–3046.
Lascu, C. A modified direct torque control for induction motor sensor less drive // IEEE Trans. Ind. Ap-plicat. – 2000. – Vol. 36. – Pp. 122–130.
Implementation of emotional controller (BELBIC) for synchronous reluctance motor drive / E. Dary-abeigi, H. Abootorabizarchi, G. Arabmarkadeh, M. Rahman, C. Lucas // IEEE International Electric Ma-chines & Drives Conference (IEMDC). – 2011. – Pp. 1088–1093.
Daryabeigi, E. Emotional controller in Electric Drives – A Review / E. Daryabeigi, G. Arab markadeh, C. Lucas // IEEE, IECON, 2010. – Pp. 2901–2907.
Emotional neural networks with universal approximation property for stable direct adaptive nonlinear control systems / F. Baghbani, M. Akbarzadeh, M. Sistani, A. Akbarzadeh // Engineering Applications of Artificial Intelligence, 2002. – Vol. 89.
Wu, Q. Self-Organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots // IEEE Access, –2018. – Vol. 6. – № 59. – Pp. 96–108.
Marr, D. A theory of cerebellar cortex // The Journal of Physiology. – 1969. – Vol. 2002. – № 2. – Pp. 437–470.
Albus, J. New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC) // Journal of Dynamic Systems, Measurement, and Control. – 1975. – Vol. 97. – № 3. – P. 220.
Albus, J. Data Storage in the Cerebellar Model Articulation Controller (CMAC) // Journal of Dynamic Systems, Measurement, and Control. – 1975. – Vol. 97. – № 3. – P. 228.
Wang, L. Fuzzy systems are universal approximators // Proc. IEEE Int. Conf. On Fuzzy Systems, 1992. – Pp. 1163–1170.
Medical sample classifier design using fuzzy cerebellar model neural networks / H. Li, R. Yeh, Y. Lin, L. Lin // Acta Polytechnica Hungarica. – 2004. – Vol. 13. – № 6. – Pp. 7–24.
Lee, C. An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchroniza-tion / C. Lee, F. Chang, C. Lin // IEEE Transactions on Cybernetics. – 2014. – Vol. 44. – № 3. – Pp. 329–341.
Chung, C. Fuzzy Brain Emotional Cerebellar Model Articulation Control System Design for Multi-Input Multi-Output Nonlinear / C. Chung, C. Lin // Acta Polytechnica Hungarica. – 2015. – Vol. 12. – № 4. – Pp. 39–58.
Xu, S. Research and Application of the Pellet Grate Thickness Control System Base on Improved CMAC Neural Network Algorithm / S. Xu, Y. Jing // Journal of Residuals Science & Technology. – 2016. – Vol. 13. – № 6. – Pp. 1–9.
Cheng, H. The Fuzzy CMAC Based on RLS Algorithm // Applied Mechanics and Materials. – 2013. – Vol. 432. – Pp. 478–782.
Huber, P. Robust Statistics / P. Huber, E. Ronchetti. // 2nd ed. Wiley, 2009. – P. 380.
Hybrid Neural Network Cerebellar Model Articulation Controller Design for Non-linear Dynamic Time-Varying Plants / T. Le, T. Huynh, S. Hong, C. Lin // Frontiers in Neuroscience, 2020. – Vol. 14. – P. 695.
Lin, C. WCMAC-based control system design for nonlinear systems using PSO / C. Lin, T. Le // Intel. Fuzzy Syst. – 2017. – Vol. 33. – Pp. 807–818.
Lin, C. Adaptive TOPSIS fuzzy CMAC back-stepping control system design for nonlinear systems / C. Lin, T. Huynh, T. Le // Computer. – 2018. – Vol. 23. – Pp. 6947–6966.
Lin, C. DC–DC converters design using a type- 2 wavelet fuzzy cerebellar model articulation controller / C. Lin, V. La, T. Le // Neural Comput. Appl. – 2004. – Vol. 32. – Pp. 2217–2229.
Sun, Y. A Fuzzy Brain Emotional Learning Classifier Design and Application in Medical Diagnosis / Y. Sun, C. Lin // Acta Polytechnica Hungarica. – 2019. – Vol. 16. – № 4.
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring / Е. Golub, В. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. Mesirov, H. Coller, J. Downing, N. Loh, M. Caligiuri, C. Bloomfield, E. Lander // Science. – 1999. – Vol. 86. – № 5439. – Pp. 531–537.
Zhao, J. Wavelet Fuzzy Brain Emotional Learning Control System Design for MIMO Uncertain Nonlin-ear Systems / J. Zhao, C. Lin, F. Chao // Frontiers in Neuroscience. – 2019. – Vol. 12.
A high-performance spelling system based on EEG-EOG signals with visual feedback / M. Lee, J. Wil-liamson, D. Won, S. Fazli, S. Lee // IEEE Trans. Neural Syst. Rehabil. – 2018. – Vol. 26. – № 7. – Pp. 1443–1459.
Kwak, N. A convolutional neural network for steady state visual evoked potential classification under ambulatory environment / N. Kwak, K. Muller, S. Lee. // PLoS One. – 2017. – Vol. 12. – № 2. –578 p.
Design of EEG-based Drone Swarm Control System using Endogenous BCI Paradigms / D. Lee, H. Ahn, J. Jeong, S. Lee // EEE The 9th International Winter Conference on Brain-Computer Interface, 2020.
Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions / J. Jeong, J. Cho, K. Shim, B. Kwon, B. Lee, D. Lee, D. Lee, S. Lee // Giga Science. – 2020. – Vol. 9. – № 10. – P. 98.
Implementation of Brain Emotional Learning-Based Intelligent Controller for Flocking of Multi-Agent Systems / M. Jafari, R. Fehr, C. Garcia, L. Rodolfo, Q. Espinoza, E. Steed, N. Xu // IFAC-Papers On-Line. 50, 2017. – Vol. 50. – Pp. 6934–6939.
Ульянов, С. В. Интеллектуальная система оценки эмоций оператора – инструментарий обработки ЭЭГ / С. В. Ульянов, А. А. Мамаева, А. В. Шевченко. // Медицинская Техника. – 2020. – Т. 2. – С. 48–51.
Ульянов, С. В. Когнитивное интеллектуальное управление. Часть I: Система оценки эмоций оператора с применением глубокого машинного обучения на основе мягких вычислений / С. В.Ульянов, А. А. Мамаева, А. В. Шевченко // Робототехника и техническая кибернетика. – 2020. – Т. 8. – № 3. – С. 217–232.
Mamaeva, A. A. Human Being Emotion in Cognitive Intelligent Robotic Control Pt I: Quantum / Soft Computing Approach / A. A. Mamaeva, A. V. Шевченко, S. V. Ulyanov. // Artificial Intelligence Ad-vances, 2020. – Vol. 2. – No 1. – Pp. 1–30.
Литвинцева, Л. В. Технологии интеллектуальных вычислений : учебно-методическое пособие. Ч. 1 : Мягкие и дробные вычисления / Л.В. Литвинцева, О.Ю. Тятюшкина, С.В. Ульянов; Рец. А.П.Рыжов [и др.]. – М. : КУРС, 2020. – 288 с.
Иванцова, О. В. Технологии интеллектуальных вычислений : учебно-методическое пособие. Ч. 2 : Квантовые вычисления и алгоритмы. Квантовый алгоритм самоорганизации. Квантовый нечеткий вывод / О.В. Иванцова, В.В. Кореньков, С.В. Ульянов; Рец. А.П.Рыжов [и др.]. – М. : КУРС, 2020. – 296 с.
Кореньков, В. В. Технологии интеллектуальных вычислений : учебно-методическое пособие. Ч. 3 : Квантовая информационная самоорганизация неточных знаний в квантовой программной инже-нерии / В.В. Кореньков, М.Н. Левин, С.В. Ульянов; Рец. А.П. Рыжов [и др.]. – М. : КУРС, 2020. – 288 с.
Левин, М. Н. Технология мягких вычислений. Часть 1: Интеллектуальная программная инженерия (нечеткие системы, нейронные сети и генетические алгоритмы) : учебно-методическое пособие / М.Н. Левин, Л.В. Литвинцева, С.В. Ульянов. – М. : КУРС, 2020. – 336 с.
Левин, М.Н. Технология мягких вычислений. Часть 2: Программная системная инженерия в интеллектуальной робототехнике: научно-методическое пособие / М.Н. Левин, О.Ю. Тятюшкина, С.В. Ульянов. – М. : КУРС, 2020. – 336 с.
Технология мягких вычислений. Часть 3: Введение в интеллектуальную робототехнику: научно-методическое пособие / А.В. Николаева, А.Г. Решетников, В.С. Ульянов, С.В. Ульянов. – М. : КУРС, 2021. – 408 с.
Korenkov, V.V. Quantum Software Engineering (Background). Part I. Mathematical background of gate-based software engineering: Educational and methodical textbook // V.V. Korenkov, M.N. Levin, S.V. Ulyanov. – M. : KURS, 2021. – 368 p.
Korenkov, V.V. Quantum Software Engineering (Background). Part II. End-to-end intelligent design IT of quantum algorithms: Educational and methodical textbook / V.V. Korenkov, A.G. Reshetnikov, S.V. Ulyanov. – M. : KURS, 2021. – 416 p.
Ivancova, O.V. Quantum Software Engineering. Quantum supremacy modelling. Part I: Design IT and information analysis of quantum algorithms: Educational and methodical textbook // O.V. Ivancova, V.V. Korenkov, S.V. Ulyanov: Textbook – Dubna: Joint Institute for Nuclear Researches / INESYS (EFKO Group). – М. : KURS, 2020. – 328 p.
Ivancova, O.V. Quantum Software Engineering. Quantum supremacy modelling. Part II: Quantum search algorithms simulator – computational intelligence toolkit: Educational and methodical textbook // O.V. Ivancova, V.V. Korenkov, S.V. Ulyanov. – M. : KURS, 2020. – 344 p.

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