The Wiener nonlinear system is composed of a linear dynamic subsystem in series with a static nonlinear subsystem. This type of system is widely found in the petroleum, chemistry, thermal and other process industries. It is of great significance to obtain the parameter estimates of the Wiener systems. This paper studies the identification problem of the Wiener time delay nonlinear system. Based on the gradient search principle, a stochastic gradient identification algorithm and a gradient-based iterative identification algorithm are derived. Furthermore, a linearly decreasing weight particle swarm iterative identification algorithm is also proposed for the discussed Wiener time delay systems. Finally, a numerical example and two application cases are given for validating the feasibility of the three identification methods. The results demonstrate that the three algorithms can identify the unknown parameters of the Wiener model effectively. Moreover, the linearly decreasing weight particle swarm iterative identification algorithm behaves much better than the stochastic gradient and the gradient-based iterative algorithms in accuracy and convergence speed.

中文翻译：

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基于粒子群迭代和梯度搜索原理的维纳系统参数估计

维纳非线性系统由线性动态子系统与静态非线性子系统串联组成。这种类型的系统广泛存在于石油、化学、热力和其他加工工业中。获取维纳系统的参数估计具有重要意义。本文研究了Wiener时滞非线性系统的辨识问题。基于梯度搜索原理，推导出随机梯度识别算法和基于梯度的迭代识别算法。此外，还针对所讨论的维纳时延系统提出了一种线性递减权重粒子群迭代识别算法。最后，给出了一个数值例子和两个应用案例，以验证三种识别方法的可行性。结果表明，三种算法均能有效识别Wiener模型的未知参数。此外，线性递减权重粒子群迭代识别算法在精度和收敛速度上都比随机梯度和基于梯度的迭代算法表现得更好。