论文标题
使用基于导数的正则神经网络的NARX识别
NARX Identification using Derivative-Based Regularized Neural Networks
论文作者
论文摘要
这项工作提出了一种新的正则化方法，用于鉴定非线性自回归外源性（NARX）模型。正则化方法促进了过去输入样品对当前模型输出的影响的指数衰减。这是通过惩罚NARX模型模拟输出相对于过去输入的敏感性来完成的。这促进了估计模型的稳定性，并提高了获得的模型质量。通过模拟示例证明了该方法的有效性，其中使用这种新方法识别神经网络NARX模型。此外，与其他正则化方法和模型类相比，提出的正则化方法在模拟误差性能方面提高了模型的准确性。
This work presents a novel regularization method for the identification of Nonlinear Autoregressive eXogenous (NARX) models. The regularization method promotes the exponential decay of the influence of past input samples on the current model output. This is done by penalizing the sensitivity of the NARX model simulated output with respect to the past inputs. This promotes the stability of the estimated models and improves the obtained model quality. The effectiveness of the approach is demonstrated through a simulation example, where a neural network NARX model is identified with this novel method. Moreover, it is shown that the proposed regularization approach improves the model accuracy in terms of simulation error performance compared to that of other regularization methods and model classes.
