通过间隔类型-2 T-S 模糊框架对不确定的延迟遗传调节网络进行扩展的消耗性分析
IEEE transactions on cybernetics
|January 19, 2026
概括
这项研究引入了一种新的间隔类型-2模糊方法,用于分析具有延迟的不确定的遗传调节网络 (GRNs). 该方法提高了这些复杂的生物系统的稳定性和控制设计.
科学领域:
- 系统生物学 系统生物学
- 控制理论 控制理论
- 计算生物学 计算生物学
背景情况:
- 遗传调节网络 (GRNs) 呈现出复杂的动态和参数不确定性.
- 延迟的系统和不确定性在GRN建模和控制中带来了重大挑战.
- 与传统的模糊集相比,间隔类型-2 (IT2) 模糊逻辑提供了更强大的处理不确定性的能力.
研究的目的:
- 使用IT2 Takagi-Sugeno (T-S) 模糊框架,为不确定的延迟GRNs开发扩展散散分析.
- 通过IT2模糊集,建立GRN动态和参数不确定性的可靠表示.
- 为了获得最大允许延迟的明确边界,并验证性能指标.
主要方法:
- 构建一个小说Lyapunov-Krasovskii (L-K) 的功能.
- 开发一个统一的分析框架,用于{L}_{2}$ - {L}_{infty }$性能,减弱,被动性和消散性验证.
- 实施一个会员功能依赖 (MFD) 稳定性标准,利用IT2模糊集特征.
主要成果:
- 获得了允许的最大延迟极限的明确推导.
- 拟议的IT2模糊框架提供了一个强大的GRN动态的表示.
- 数字示例显示,与现有方法相比,稳定性条件较为保守,散射率指数改善.
结论:
- 该研究为GRN分析提供了首个IT2模糊方法,有效地处理不确定性和延迟.
- 开发的方法提升了对GRNs强有力的控制策略的理论理解和实际设计.
- 这项工作在合成生物学中具有潜在的应用,用于设计更可靠的生物电路.
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