最佳状态翻转控制和学习,用于同步概率布尔网络
Chenyang Bian1, Zhipeng Zhang2, Leihao Du3
1School of Control Science and Engineering, Tiangong University, Tianjin, 300387, China.
ISA transactions
|June 5, 2025
概括
这项研究在使用最佳状态翻转控制和Q学习的概率布尔网络 (PBNs) 中实现了保证的同步. 这些方法有效地解决了大型PBN中复杂的同步问题.
科学领域:
- 计算生物学 计算生物学
- 控制理论 控制理论
- 人工智能的人工智能
背景情况:
- 概率布尔网络 (PBNs) 是复杂的系统,在基因调节网络和人工智能中具有应用.
- 在PBN中实现保证同步是一个具有挑战性的问题,因为它们固有的概率性质.
- 现有的方法经常与大规模PBNs的计算复杂性作斗争.
研究的目的:
- 开发一种可靠的方法,在PBN中实现与概率为1的同步.
- 为了将同步问题转化为更易处理的集合稳定问题,使用半张量产品 (STP) 框架.
- 为了提高小型和大型PBN的计算效率.
主要方法:
- 该研究使用半张力产物 (STP) 来将PBN同步问题转换为集稳定问题.
- 最佳的状态翻转控制策略与Q学习相结合,以实现高效的同步.
- 引入了基于状态翻转的可达设置标准,以确定最佳的翻转序列.
- 为大规模PBN提出了两步的Q学习优化策略,涉及Q表生成和最佳序列列表.
主要成果:
- 提出的方法成功地在PBNs中实现了与概率为1的同步.
- 一个新的可达集合标准通过识别最佳状态翻转序列来提高计算效率.
- 基于Q学习的策略显著降低了大规模PBN同步的计算复杂性.
- 数字模拟验证了开发算法的有效性和实用性.
结论:
- 集成最佳状态翻转控制和Q学习为PNN同步提供了有效的解决方案.
- STP框架和可达到的标准集为PNB分析提供了一个计算效率高的方法.
- 提出的方法是实用的和可扩展的,在复杂的生物和人工系统中显示出应用的巨大潜力.
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