Time-optimal open-loop set stabilization of Boolean control networks

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Networks Pub Date : 2024-09-03 DOI:10.1016/j.neunet.2024.106694
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Abstract

We show that for stabilization of Boolean control networks (BCNs) with unobservable initial states, open-loop control and close-loop control are not equivalent. An example is given to illustrate the nonequivalence. Enlightened by the nonequivalence, we explore open-loop set stabilization of BCNs with unobservable initial states. More specifically, this issue is to investigate that for a given BCN, whether there exists a unified free control sequence that is effective for all initial states of the system to stabilize the system states to a given set. The criteria for open-loop set stabilization is derived and for any open-loop set stabilizable BCN, every time-optimal open-loop set stabilizer is proposed. Besides, we obtain the least upper bounds of two integers, which are respectively related to the global stabilization and partial stabilization of BCNs in the results of two literature articles. Using the methods in the two literature articles, the least upper bounds of the two integers cannot be obtained.

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布尔控制网络的时间最优开环集稳定
我们证明,对于初始状态不可观测的布尔控制网络(BCN)的稳定问题,开环控制和闭环控制并不等同。我们举例说明了不等价性。在非等价性的启发下,我们探讨了具有不可观测初始状态的布里控制网络的开环集稳定问题。更具体地说,这个问题是要研究对于给定的 BCN,是否存在一个统一的自由控制序列,该序列对系统的所有初始状态都有效,能将系统状态稳定在给定的集合上。我们推导了开环集稳定的标准,并针对任何可开环集稳定的 BCN,提出了每一个时间最优开环集稳定器。此外,我们还得到了两个整数的最小上界,这两个整数分别与两篇文献结果中 BCN 的全局稳定和局部稳定有关。使用这两篇文献中的方法,无法得到这两个整数的最小上界。
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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