基于采样数据的随机连续时间Wiener模型在线辨识
Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Data
AI总结：
针对随机Wiener模型辨识中忽略扰动致估计有偏、似然类方法对分布假设敏感且复杂度高的问题，提出一种基于输出误差预测器的递推在线估计算法，通过随机逼近实现连续时间随机Wiener模型辨识，适用于通用参数化且对扰动频谱假设鲁棒。
摘要：
众所周知，在随机Wiener模型辨识中忽略随机扰动的存在会导致估计量渐近有偏。另一方面，通过基于似然的方法实现的最优统计辨识对数据分布的假设较为敏感，且通常依赖相对复杂的序贯蒙特卡洛算法。我们提出了一种基于输出误差预测器的简单递推在线估计算法，通过随机逼近方法辨识连续时间随机参数化Wiener模型。该方法适用于通用模型参数化形式，且数值仿真示例表明，其对扰动过程频谱的假设具有鲁棒性。
英文摘要：
It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators. On the other hand, optimal statistical identification, via likelihood-based methods, is sensitive to the assumptions on the data distribution and is usually based on relatively complex sequential Monte Carlo algorithms. We develop a simple recursive online estimation algorithm based on an output-error predictor, for the identification of continuous-time stochastic parametric Wiener models through stochastic approximation. The method is applicable to generic model parameterizations and, as demonstrated in the numerical simulation examples, it is robust with respect to the assumptions on the spectrum of the disturbance process.
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