An Improved Alpha Beta Filter Using a Deep Extreme Learning Machine

Junaid Khan,Muhammad Fayaz,Jeonghwan Gwak,Wali Khan Mashwani,Ayyaz Hussain,Shah Khalid

doi:10.1109/access.2021.3073876

Abstract

This paper introduces new learning to the prediction model to enhance the prediction algorithms’ performance in dynamic circumstances. We have proposed a novel technique based on the alpha-beta filter and deep extreme learning machine (DELM) algorithm named as learning to alpha-beta filter. The proposed method has two main components, namely the prediction unit and the learning unit. We have used the alpha-beta filter in the prediction unit, and the learning unit uses a DELM. The main problem with the conventional alpha-beta filter is that the values are generally selected via the trial-and-error technique. Once the alpha-beta values are chosen for a specific problem, they remain fixed for the entire data. It has been observed that different alpha-beta values for the same problem give different results. Hence it is essential to tune the alpha-beta values according to their historical behavior for certain values. Therefore, in the proposed method, we have addressed this problem and added the learning module to the conventional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> filter to improve the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> filter’s performance. The DELM algorithm has been used to enhance the conventional alpha-beta filter algorithm’s performance in dynamically changing conditions. The model performance has been measured using indoor environmental values of temperature and humidity. The relative improvement in the proposed learning prediction model’s accuracy was 7.72% and 16.47% in RMSE and MSE metrics. The results show that the proposed model outperforms in terms of the result as compared to the conventional alpha-beta filter.

Highlights

It is essential to predict accurate and reliable parameters for the energy consumption prediction algorithms
This paper has proposed a new methodology for tuning the alpha-beta filter parameters based upon a deep extreme learning machine and named it as learning to alpha-beta filter
The fixation of values is the main weakness of this algorithm because different alpha-beta filter provides different results on different alpha-beta filter values

Summary

Introduction

It is essential to predict accurate and reliable parameters for the energy consumption prediction algorithms. The associate editor coordinating the review of this manuscript and approving it for publication was Zhan-Li Sun. it is essential to estimate future values accurately because any wrong estimation can lead to unforeseen consequences and performance decreases in the prediction algorithms [1]. The simple way to predict the future values is to analyze the data, acquire some results, and based on results, predict future values. This kind of practice requires a lot of training time; an intelligent system is always needed to make correct decisions in a short time

Methods

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Discussion

Conclusion