IT Modeling of Self-Organizing Intelligent Controllers Based on Quantum Deep Machine Learning
| Autor: | Ulyanov, S.V., Reshetnikov, A.G., Zrelova, D.P. |
|---|---|
| Jazyk: | ruština |
| Rok vydání: | 2023 |
| Předmět: |
quantum intelligent controller
quantum algorithm gate quantum deep machine learning classical efficient simulation intelligent robotics квантовый интеллектуальный контроллер квантовая алгоритмическая ячейка квантовое глубокое машинное обучение эффективный классический симулятор интеллектуальная робототехника Electronic computers. Computer science QA75.5-76.95 |
| Zdroj: | Современные информационные технологии и IT-образование, Vol 19, Iss 2, Pp 365-380 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2411-1473 |
| DOI: | 10.25559/SITITO.019.202302.365-380 |
| Popis: | The physical interpretation of the process of controlling self-organization at the quantum level is discussed on the basis of quantum information-thermodynamic models of exchange and extraction of quantum (hidden) valuable information from/between classical particle trajectories in the "swarm of interacting particles" model. The main physical and information-thermodynamic aspects of the model of quantum intelligent control of classical control objects are discussed and described. An approach is considered for constructing reference control models based on new laws of quantum deep machine learning applying Lagrange/Hamilton neural networks. This work develops the approach of self-organized intelligent control, describing the strategy of designing intelligent cognitive control systems based on quantum and soft computing. The synergetic effect of the quantum self-organization of the knowledge base, extracted from the non-robust knowledge bases of the intelligent fuzzy controller, is demonstrated. The information-thermodynamic law of quantum self-organization of the optimal distribution of the basic qualities of control (stability, controllability and robustness) and the law of quantum information thermodynamics on the possibility of extracting additional useful work based on the extracted quantum information hidden in classical states are applied. Formed (without violating the second law of quantum thermodynamics) on the basis of the extracted amount of hidden quantum information, the "thermodynamic" control force allows the robot (as an object of control) to perform quantitatively more useful work compared to the amount of work spent (on extracting quantum hidden information). The guaranteed achievement of the goal of controlling the robot is carried out on the basis of a designed intelligent cognitive control system using the quantum knowledge base optimizer – QCOptKBTM, the structure of which includes a quantum fuzzy inference – QFI. The quantum algorithm of self-organization of non-robust QFI knowledge bases is structurally based on the synergetic effects of hidden quantum information to implement the optimal distribution of management qualities. This technology makes it possible to increase the reliability of intelligent cognitive control systems in control situations under uncertainty. The examples demonstrated the effectiveness of introducing the QFI scheme as a ready-made programmable algorithmic solution for embedded intelligent control systems. |
| Databáze: | Directory of Open Access Journals |
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