An Exponentially Weighted Moving Average Method for Identification and Monitoring of Stochastic Systems

2008 ◽  
Vol 47 (21) ◽  
pp. 8239-8249 ◽  
Author(s):  
Shyh-Hong Hwang ◽  
Ho-Tsen Chen ◽  
Chuei-Tin Chang
Keyword(s):  
Average Method ◽  
Stochastic Systems ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Weighted Moving Average ◽  
Exponentially Weighted

scholarly journals Comparative Study on Exponentially Weighted Moving Average Approaches for the Self-Starting Forecasting

2020 ◽  
Vol 10 (20) ◽  
pp. 7351
Author(s):  
Jaehong Yu ◽  
Seoung Bum Kim ◽  
Jinli Bai ◽  
Sung Won Han
Keyword(s):  
Comparative Study ◽  
Computational Efficiency ◽  
Average Method ◽  
Moving Average ◽  
The Self ◽  
Superior Performance ◽  
Real Case ◽  
Exponentially Weighted Moving Average ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Recently, a number of data analysists have suffered from an insufficiency of historical observations in many real situations. To address the insufficiency of historical observations, self-starting forecasting process can be used. A self-starting forecasting process continuously updates the base models as new observations are newly recorded, and it helps to cope with inaccurate prediction caused by the insufficiency of historical observations. This study compared the properties of several exponentially weighted moving average methods as base models for the self-starting forecasting process. Exponentially weighted moving average methods are the most widely used forecasting techniques because of their superior performance as well as computational efficiency. In this study, we compared the performance of a self-starting forecasting process using different existing exponentially weighted moving average methods under various simulation scenarios and real case datasets. Through this study, we can provide the guideline for determining which exponentially weighted moving average method works best for the self-starting forecasting process.

scholarly journals Design and performance evaluation of an exponentially weighted moving average–based adaptive control for piezo-driven motion platform

2018 ◽  
Vol 10 (6) ◽  
pp. 168781401876719
Author(s):  
Yung Ting ◽  
Tho Van Nguyen ◽  
Jia-Ci Chen
Keyword(s):  
Temperature Rise ◽  
Average Method ◽  
Adaptive System ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Model Reference Adaptive System ◽  
Predictor Corrector ◽  
Model Reference Adaptive ◽  
Weighted Moving Average ◽  
Exponentially Weighted

In this article, building a controlled system with velocity feedback in the inner loop for a platform driven by piezoelectric motors is investigated. Such a motion control system is subject to disturbance such as friction, preload, and temperature rise in operation. Especially, temperature rise is an essential problem of using piezoelectric motor, but very few research works address this topic in depth. Exponentially weighted moving average method has been widely used in process control to deal with systematic change and drift disturbance. It is attempted to map the exponentially weighted moving average method and the predictor corrector control with two exponentially weighted moving average formulas into a run-to-run model reference adaptive system for velocity control. Using a predictive friction model, a dead-zone compensator is built that can reduce the friction effect and provide an approximately linear relation of the input voltage and the output velocity for the subsequent exponentially weighted moving average or predictor corrector control control design. Comparison of the exponentially weighted moving average, predictor corrector control, and proportional–integral–derivative controllers is carried out in experiment with different speed patterns on a single-axis and a bi-axial platform. The results indicate that the proposed run-to-run-model reference adaptive system predictor corrector control is superior to the other methods.

Statistical Process Control Based on Kalman Filter in Manufacturing Process

2011 ◽  
Vol 201-203 ◽  
pp. 986-989
Author(s):  
Pei Wang ◽  
Ding Hua Zhang ◽  
Shan Li ◽  
Ming Wei Wang ◽  
Bing Chen
Keyword(s):  
Kalman Filter ◽  
Process Control ◽  
Statistical Process Control ◽  
Manufacturing Process ◽  
Average Method ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Statistical Process ◽  
Weighted Moving Average ◽  
Exponentially Weighted

In order to reduce the impact of data noise to quality control and make monitor results more precise in manufacturing process, the method of statistical process control based on Kalman filter was proposed. In this method, the statistical process control model based on Kalman filter was built and the quality control method of exponentially weighted moving average based on Kalman filter was put forward. While monitoring manufacturing process, first the technology of Kalman filter was used to smooth data and to reduce noise, and then control charts were built by the method of exponentially weighted moving average to monitor quality. Finally, the performance of the exponentially weighted moving average method based on Kalman filter and the tranditional exponentially weighted moving average method was compared. The performance result illustrates the feasibility and validity of the proposed quality monitor method.

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