METHOD OF BROWN’S EXPONENTIAL FILTER ADAPTATION BY USING THE METHOD OF LEAST SQUARES

Authors

  • B. R. Boriak Poltava National Technical Yuri Kondratyuk University, Poltava
  • A. M. Silvestrov National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv
  • V. V. Lutsio Poltava National Technical Yuri Kondratyuk University, Poltava

DOI:

https://doi.org/10.18372/1990-5548.54.12306

Keywords:

Exponential smoothing, noise, forecast, tracking signal, low-pass filter, smoothing factor, least squares

Abstract

Proposed structure of the filtering algorithm gives an opportunity to avoid some of disadvantages of exponential smoothing. The main aim of proposed algorithm is to estimate filtration quality and get an ability to change smoothing factor during the work of the system. We used the method of least squares to estimate the difference between smoothed signal and signal that was built from filtered signal got by its approximation. This method might be used in the case if the trajectory of the tracking signal is not changing during the estimating process or it might be changed inconsiderably. This data processing algorithm can be used as filtering and forecasting system and integrated in systems with lags.

Author Biographies

B. R. Boriak, Poltava National Technical Yuri Kondratyuk University, Poltava

Post-graduate student

A. M. Silvestrov, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv

Doctor of Engineering Science. Professor

V. V. Lutsio, Poltava National Technical Yuri Kondratyuk University, Poltava

Post-graduate student

References

B. R. Boriak, and A. M. Silvestrov, “Filtering and forecasting signals algorithm based on exponential Brown’s filter,” Academic J. Control, Navigation and Communication Systems, vol. 4, no. 44, Poltava National Technical Yuri Kondratyuk University, Poltava, Ukraine, pp. 150–152, 2017.

R. G. Brown, “Exponential Smoothing for Predicting Demand,” Cambridge, Massachusetts, USA https://www.industrydocumentslibrary.ucsf.edu/tobacco/docs/#id=jzlc0130, 15 p.

NIST/SEMATECH e-Handbook of Statistical Methods, “Double Exponential Smoothing”, http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc433.htm, 1 p.

Downloads

Issue

Vol. 4 No. 54 (2017)

Section

THEORY AND METHODS OF SIGNAL PROCESSING

License

Authors who publish with this journal agree to the following terms:

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).