Error Reduction of Parametric Auto Regressive Moving Average Estimation Algorithm to Reach Optimal Point
In the linear and nonlinear multi-input and multi-output systems, there are several methods for identifying and estimating system parameters. The method of detecting system parameters in a simulated autocorrelated system with a minimized error between the target data and output results is affected by noise. After adding the summed noise to the input data, the system coefficients are determined in the least-squares algorithm. The trend of system error changes decreases with increasing number of input samples. However, it does not mean that the estimation error decreases by increasing iteration during an increasing trend; where, the number of samples given to the system in order to obtain a specific error does not decrease with increasing number of replications. In addition, reducing the estimation error of the system is dependent on the input data more than the data replication.
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