How to Interpret the Mean & Standard Deviation on a Control Chart. The extent to which products meet specifications needs to be systematically monitored in a production process.

MSE measures the average squared difference between predicted and actual values, it helps quantify a model's accuracy. Lower MSE indicates better fit, making it a key metric for comparing models.

In this paper, a new control mechanism for the variable forgetting factor (VFF) of the recursive least square (RLS) adaptive algorithm is presented. The control ...

One of the main advantages of MSE is its ability to highlight larger errors within your dataset. This characteristic makes it particularly useful when large errors are undesirable and should be ...

Then, applying the filtered-x least mean square (FxLMS) algorithm to ErANC for designing the controller, a novel ErFxLMS algorithm is developed to improve the noise reduction performance of FxLMS.