通过差分神经网络对一类不确定的交换系统进行状态识别
Isaac Chairez1, Alejandro Garcia-Gonzalez2, Alberto Luviano-Juarez3
1Institute of Advanced Materials for the Sustainable Manufacturing, Tecnologico de Monterrey, Zapopan, Jalisco, México.
概括
这项研究引入了适应性神经网络标识符,用于不确定的交换非线性系统. 该方法确保识别错误的趋同,得到了利亚普诺夫理论的验证,并在机械和步态模型上进行了测试.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 非线性动力学 不线性动力学
背景情况:
- 交换非线性系统由于其复杂的动态,存在重大识别挑战.
- 适应控制和神经网络为不确定性下的系统识别提供了潜在的解决方案.
研究的目的:
- 开发使用连续时间神经网络的不确定交换非线性系统的非参数识别方案.
- 确保识别错误的趋同,并分析系统稳定性.
- 为适应性识别器调整引入新的学习规律.
主要方法:
- 使用连续神经网络标识符进行非参数系统识别.
- 应用了莱普诺夫理论和切换系统的实用稳定性变量,以确保错误的融合.
- 开发了两个学习定律:一个基于稳定性分析,另一个用于有限时间趋同.
主要成果:
- 适应式标识符证明了识别错误的趋同到源地附近的一个小区域.
- 汇聚区域的上界以系统不确定性和噪音为特征.
- 对于机械系统和人类步行模型,都取得了准确的识别结果.
结论:
- 拟议的非参数识别方案对于不确定的交换非线性系统是有效的.
- 该方法为分析这些系统中的自适应神经网络标识符提供了正式的技术.
- 这种方法对机器人,生物力学和控制工程的应用具有前景.
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