针对静态半马科夫跳跃神经网络与非脆弱隐藏信息和执行器和度平均平方同步的优化控制
概括
这项研究涉及随机隐藏的半马尔科夫跳跃神经网络与执行器和的控制和同步. 一个新的非脆弱控制器确保了可靠的同步,尽管存在不确定性.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 随机系统 随机系统 随机系统
背景情况:
- 随机隐藏的半马科夫跳跃神经网络 (SMJNNs) 呈现复杂的动态.
- 执行器和和系统不确定性挑战可靠的控制.
- 在这些网络中实现强大的同步是至关重要的.
研究的目的:
- 研究SMJNNs的异步输出反控制和H∞同步.
- 开发一个不易碎的控制器,对不确定性和模式不匹配有强大作用.
- 建立足够的条件来实现随机平均平方同步 (MSS).
主要方法:
- 一个新的NN模型集成半马尔科夫过程 (SMP),隐藏信息和布朗运动.
- 设计一个非脆弱的控制器,利用隐藏的信息来防止干扰.
- 基于SMP的Lyapunov函数的构建,以确保MSS在吸引的领域内.
主要成果:
- 获得足够的条件来实现随机平均平方同步 (MSS).
- 提出的非脆弱控制器有效地减轻了不确定性,并提高了系统可靠性.
- 通过数值示例证明控制策略的可行性.
结论:
- 开发的控制方法确保了复杂的SMJNNs的强大和可靠的H∞同步.
- 集成SMP和隐藏信息提供了一个强大的建模和控制框架.
- 这些发现有助于推进不确定的随机系统的控制理论.
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