论文标题
部分可观测时空混沌系统的无模型预测
Accelerated Performance and Accelerated Learning with Discrete-Time High-Order Tuners
论文作者
论文摘要
我们考虑了两个高阶调谐器，这些调谐器已被证明具有加速性能，一个基于Polyak的重球方法，另一个基于Nesterov的加速方法。我们表明，当回归器持续令人兴奋时，参数估计值是有界的，并将其收敛到成倍快速的真实值。与基于归一化梯度下降的算法相比，模拟结果证实了这些高阶调谐器的加速性能和加速学习特性。
We consider two high-order tuners that have been shown to have accelerated performance, one based on Polyak's heavy ball method and another based on Nesterov's acceleration method. We show that parameter estimates are bounded and converge to the true values exponentially fast when the regressors are persistently exciting. Simulation results corroborate the accelerated performance and accelerated learning properties of these high-order tuners in comparison to algorithms based on normalized gradient descent.
