This study focused on the identification problems of two-input single-output system with moving average noises based on unsupervised learning methods applied to the input signals. The input signal to the autoregressive moving average model is proposed to be arriving from a source with continuous technical and environmental changes as two separate featured input signals. These two input signals were grouped in a number of clusters using the K-means clustering algorithm. The clustered input signals were supplied to the model in an orderly fashion from cluster-1 up to cluster-K. To ensure that the output signal can be best predicted from the input signal which in turn leads to selecting good enough model for its intended use, the magnitude-squared coherence (MSC) measure is applied to the input/output signals in the cases of clustered and nonclustered inputs, which indicates best correlation coefficient when measured with clustered inputs. From collected input-output signals, we deduce a K-means clustering based recursive least squares method for estimating the parameter of autoregressive moving average system. The simulation results indicate that the suggested method is effective.

中文翻译：

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具有自回归移动平均噪声的两输入单输出模型的基于输入信号聚类的识别算法

这项研究集中在基于输入信号的无监督学习方法的移动平均噪声的两输入单输出系统的识别问题上。建议将自回归移动平均模型的输入信号作为两个独立的特征输入信号从技术和环境持续变化的源中获取。使用K均值聚类算法将这两个输入信号分为多个簇。聚类的输入信号以从聚类1到聚类K的有序方式提供给模型。为了确保可以从输入信号中最好地预测输出信号，从而又可以为其预期用途选择足够好的模型，在聚类的情况下，对输入/输出信号应用幅度平方相干（MSC）度量非聚集输入，当使用聚集输入进行测量时，它指示最佳相关系数。从收集的输入输出信号中，我们推导了一种基于K均值聚类的递归最小二乘法，用于估计自回归移动平均系统的参数。仿真结果表明该方法是有效的。