Cognitive intellignet control. Pt. 2: Quantum fuzzy inference algorithm in intelligent cognitive robotics

S. Ulyanov, Daria Zrelova, A. Shevchenko, Alla Shevchenko
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Abstract

In the first part of the article, a system for assessing operator emotions using deep machine learning based on soft computing and designing a cognitive control system was discussed. This work develops the approach of cognitive intelligent control, describing the strategy for designing intelligent cognitive control systems based on quantum and soft computing. The synergetic effect of quantum self-organization of the knowledge base, extracted from non-robust knowledge bases of an 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 a control object) to perform quantitatively more useful work compared to the amount of work expended (on extracting quantum hidden information). The guaranteed achievement of the goal of controlling the robot is carried out on the basis of the designed intelligent cognitive con-trol system using the tools of the QCOptKBTM quantum knowledge base optimizer, the structure of which includes quantum fuzzy inference (QFI). The quantum algorithm for self-organization of non-robust knowledge bases of QFI structurally relies on synergistic effects from hidden quantum information to implement the implementation of the optimal distribution of control qualities. This technology makes it possible to increase the reliability of intelligent cognitive control systems in situations of control under conditions of danger, described using a cognitive neural interface and various types of interaction with robots. The examples have demonstrated the effectiveness of intro-ducing the QFI scheme as a ready-made programmable algorithmic solution for embedded intelligent control sys-tems. The possibility of using a neural interface based on a cognitive helmet with a quantum fuzzy controller to control a vehicle is shown.
认知智能控制。第二部分:智能认知机器人中的量子模糊推理算法
在文章的第一部分中,讨论了基于软计算的深度机器学习评估操作员情绪的系统,并设计了一个认知控制系统。本工作发展了认知智能控制的方法,描述了基于量子和软计算的智能认知控制系统的设计策略。从智能模糊控制器的非鲁棒知识库中提取了量子自组织知识库,证明了知识库的协同效应。应用了量子自组织的信息热力学定律对控制的基本性质(稳定性、可控性和鲁棒性)的最优分布和量子信息热力学定律对提取的隐藏在经典状态中的量子信息提取额外有用功的可能性进行了研究。在不违反量子热力学第二定律的情况下,根据提取的隐藏量子信息的数量,“热力学”控制力使机器人(作为控制对象)能够定量地执行比(提取量子隐藏信息)所花费的工作量更多的有用工作。在设计的智能认知控制系统的基础上,利用QCOptKBTM量子知识库优化器工具实现机器人控制目标的保证,该优化器的结构包括量子模糊推理(QFI)。QFI非鲁棒知识库自组织量子算法在结构上依赖于隐藏量子信息的协同效应来实现控制质量的最优分布。这项技术可以提高智能认知控制系统在危险条件下控制的可靠性,使用认知神经接口和与机器人的各种类型的交互进行描述。实例证明了引入QFI方案作为嵌入式智能控制系统的现成可编程算法解决方案的有效性。展示了使用基于认知头盔的神经接口与量子模糊控制器来控制车辆的可能性。
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