Clustering Input Signals Based Identification Algorithms for Two-Input Single-Output Models with Autoregressive Moving Average Noises

Clustering Input Signals Based Identification Algorithms for Two-Input Single-Output Models with Autoregressive Moving Average Noises

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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 mega motion lc100 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 atlanta braves junk headband 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.