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.

翻译：众所周知，在随机维纳模型的识别中忽略随机干扰的存在会导致渐近有偏的估计量。另一方面，基于似然方法的最优统计识别对数据分布假设敏感，且通常依赖于较为复杂的序贯蒙特卡洛算法。本文基于输出误差预测器，通过随机逼近方法开发了一种简单的递归在线估计算法，用于识别连续时间随机参数维纳模型。该方法适用于一般模型参数化形式，并且如数值仿真示例所示，对于干扰过程频谱的假设具有鲁棒性。