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Электронный каталог: Ulyanov, S. V. - Quantum Soft Computing: Applied SW & HW of Robust Intelligent Robotic Controllers
Ulyanov, S. V. - Quantum Soft Computing: Applied SW & HW of Robust Intelligent Robotic Controllers
Книга (аналит. описание)
Автор: Ulyanov, S. V.
Progress in Artificial Intelligence and Pattern Recognition: Quantum Soft Computing: Applied SW & HW of Robust Intelligent Robotic Controllers
б.г.
ISBN отсутствует
Автор: Ulyanov, S. V.
Progress in Artificial Intelligence and Pattern Recognition: Quantum Soft Computing: Applied SW & HW of Robust Intelligent Robotic Controllers
б.г.
ISBN отсутствует
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Книга (аналит. описание)
Ulyanov, S.V.
Quantum Soft Computing: Applied SW & HW of Robust Intelligent Robotic Controllers / S.V.Ulyanov, V.Ulyanov // Progress in Artificial Intelligence and Pattern Recognition : 8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023, Varadero, Cuba, September 27–29, 2023 : Proceedings [Electronic resource] / Ed.: Y.H.Heredia, V.M.Nunez, J.R.Shulcloper. – Cham : Springer, 2024. – P. XXI. – URL: http://inis.jinr.ru/sl/NTBLIB/IWAIPR-2023-PXXI.pdf.
A generalized design strategy of intelligent robust control systems based on quantum/soft computing technologies that enhance robustness of hybrid intelligent controllers by sup-plying a self-organizing capability is described. We stress our attention to the robustness features of intelligent control systems in unpredicted control situations with the sim-ulation of Benchmark. For complex and ill-defined dynamic control objects that are not easily controlled by conventional control systems (such as P-[I]-D-controllers) — especially in the presence of fuzzy model parameters and different stochastic noises — the System of Systems Engineering methodology provides fuzzy controllers (FC) as an alternative way of control systems design. Soft computing methodologies, such as genetic algorithms (GA) and fuzzy neural networks (FNN) expand the application areas of FC by adding optimization, learning and adaptation features. But it is still difficult to design an optimal and robust intelligent control system, when its operational conditions have to evolve dramatically (aging, sensor failure and so on). Such conditions can be predicted, but it is difficult to cover such situations with a single FC. Using an unconventional computational intelligence toolkit, in this talk we propose a solution of this kind of generalization problems by introducing a self-organizing design process of robust KB - FC supported by a Quantum Fuzzy Inference (QFI) based on quantum soft computing ideas – (Lecture Notes in Computer Science ; Vol. 14335) .
Спец.(статьи,препринты) = Ц 84 а2 - Многомашинные комплексы вычислительных средств. Вычислительные системы и сети. Параллельные вычисления. Квантовые компьютеры
Спец.(статьи,препринты) = С 325.1а - Нейронные сети и клеточные автоматы
ОИЯИ = ОИЯИ (JINR)2024
Ulyanov, S.V.
Quantum Soft Computing: Applied SW & HW of Robust Intelligent Robotic Controllers / S.V.Ulyanov, V.Ulyanov // Progress in Artificial Intelligence and Pattern Recognition : 8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023, Varadero, Cuba, September 27–29, 2023 : Proceedings [Electronic resource] / Ed.: Y.H.Heredia, V.M.Nunez, J.R.Shulcloper. – Cham : Springer, 2024. – P. XXI. – URL: http://inis.jinr.ru/sl/NTBLIB/IWAIPR-2023-PXXI.pdf.
A generalized design strategy of intelligent robust control systems based on quantum/soft computing technologies that enhance robustness of hybrid intelligent controllers by sup-plying a self-organizing capability is described. We stress our attention to the robustness features of intelligent control systems in unpredicted control situations with the sim-ulation of Benchmark. For complex and ill-defined dynamic control objects that are not easily controlled by conventional control systems (such as P-[I]-D-controllers) — especially in the presence of fuzzy model parameters and different stochastic noises — the System of Systems Engineering methodology provides fuzzy controllers (FC) as an alternative way of control systems design. Soft computing methodologies, such as genetic algorithms (GA) and fuzzy neural networks (FNN) expand the application areas of FC by adding optimization, learning and adaptation features. But it is still difficult to design an optimal and robust intelligent control system, when its operational conditions have to evolve dramatically (aging, sensor failure and so on). Such conditions can be predicted, but it is difficult to cover such situations with a single FC. Using an unconventional computational intelligence toolkit, in this talk we propose a solution of this kind of generalization problems by introducing a self-organizing design process of robust KB - FC supported by a Quantum Fuzzy Inference (QFI) based on quantum soft computing ideas – (Lecture Notes in Computer Science ; Vol. 14335) .
Спец.(статьи,препринты) = Ц 84 а2 - Многомашинные комплексы вычислительных средств. Вычислительные системы и сети. Параллельные вычисления. Квантовые компьютеры
Спец.(статьи,препринты) = С 325.1а - Нейронные сети и клеточные автоматы
ОИЯИ = ОИЯИ (JINR)2024
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