先进的最佳跟踪集成一个神经批评技术不对称的受约束的零和游戏
Menghua Li1, Ding Wang1, Jin Ren1
1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China; Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing 100124, China; Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing 100124, China.
这项研究引入了一个神经批评方法,用于非线性连续时间的零和游戏与不对称的约束,提供了一个新的方法,以优化跟踪控制. 该技术增强了稳定性分析和控制矩阵的灵活性.
科学领域:
- 控制理论 控制理论
- 游戏理论 游戏理论
- 人工智能的人工智能
背景情况:
- 非线性连续时间零和游戏 (ZSGs) 提出了复杂的最佳跟踪挑战.
- 对于不对称约束的现有方法通常对控制矩阵施加严格的限制.
研究的目的:
- 开发一种改进的算法,以在具有不对称约束的非线性连续时间多人ZSGs中进行最佳跟踪.
- 解决以前处理不对称约束的方法的局限性.
主要方法:
- 利用神经批评技术来跟踪控制.
- 开发一种新的非二次函数来管理不对称的约束.
- 使用单个关键神经网络和正常化最的下降来进行体重更新.
主要成果:
- 导出最佳控制,最糟糕的干扰,以及跟踪汉密尔顿 - 雅各比 - 艾萨克斯方程.
- 通过使用关键网络,对最佳控制和最坏的干扰进行近似计算.
- 通过利亚普诺夫方法证明追踪和重量估计错误的稳定性.
结论:
- 提出的神经批评方法有效地解决了非线性CT ZSG与不对称约束的最佳跟踪问题.
- 这种新方法放松了对控制矩阵的限制,提供了更广泛的适用性.
- 理论结果通过两个说明性示例得到验证.
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