论文标题
部分可观测时空混沌系统的无模型预测
Kalman-Bucy-Informed Neural Network for System Identification
论文作者
论文摘要
在非线性系统中识别参数，普通微分方程对于设计强大的控制器至关重要。但是，如果系统的性质是随机的，或者仅可用嘈杂的测量值，则系统识别的标准优化算法通常会失败。我们提出了一种新方法，该方法结合了物理信息信息网络的最新进展和Kalman过滤器的众所周知成就，以便在连续时间系统中找到具有嘈杂测量值的参数。在此过程中，我们的方法允许将参数与系统状态向量的平均值和协方差矩阵一起估算。我们表明，该方法通过识别双摆的参数来适用于复杂系统。
Identifying parameters in a system of nonlinear, ordinary differential equations is vital for designing a robust controller. However, if the system is stochastic in its nature or if only noisy measurements are available, standard optimization algorithms for system identification usually fail. We present a new approach that combines the recent advances in physics-informed neural networks and the well-known achievements of Kalman filters in order to find parameters in a continuous-time system with noisy measurements. In doing so, our approach allows estimating the parameters together with the mean value and covariance matrix of the system's state vector. We show that the method works for complex systems by identifying the parameters of a double pendulum.
