论文标题
$ h^\ infty $不确定性的频域高斯流程模型
Frequency Domain Gaussian Process Models for $H^\infty$ Uncertainties
论文作者
论文摘要
贝叶斯频域系统识别用作回归的先前模型，将复合价值的高斯过程用于贝叶斯频域系统识别。如果这种过程的每个实现都是$ h_ \ infty $具有概率的功能，则可以将同一模型用于概率稳健控制，从而可以进行健壮的安全学习。我们研究了足够的条件，以使一般的复合域高斯工艺具有该特性。对于遗传学协方差为静止的过程的特殊情况，我们根据可总结的非负数序列提供了协方差结构的明确参数化。
Complex-valued Gaussian processes are used in Bayesian frequency-domain system identification as prior models for regression. If each realization of such a process were an $H_\infty$ function with probability one, then the same model could be used for probabilistic robust control, allowing for robustly safe learning. We investigate sufficient conditions for a general complex-domain Gaussian process to have this property. For the special case of processes whose Hermitian covariance is stationary, we provide an explicit parameterization of the covariance structure in terms of a summable sequence of nonnegative numbers.
