On the convergence of the LMS algorithm in adaptive filtering

Troparevsky, M. I.; D’Attellis, C.E.

doi:10.1016/j.sigpro.2004.06.004

Cited by 12 publications

(15 citation statements)

References 2 publications

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“…To the best of our knowledge, adaptive filters have rarely been applied in solar radiation estimation. Among digital filters used in applications, the adaptive filter [34,35] is advantageous compared with the finite impulse response (FIR) filter and infinite impulse response (IIR) filter due to its better performance in situations with a spectrum overlap between the signal and noise [36]. Adaptive filters are widely used in many fields such as noise canceling, system identification, and signal prediction [37,38].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Solar Radiation for Tomato Water Requirement Calculation in Chinese-Style Solar Greenhouses Based on Least Mean Squares Filter

Zhang

et al. 2019

Sensors

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The area covered by Chinese-style solar greenhouses (CSGs) has been increasing rapidly. However, only a few pyranometers, which are fundamental for solar radiation sensing, have been installed inside CSGs. The lack of solar radiation sensing will bring negative effects in greenhouse cultivation such as over irrigation or under irrigation, and unnecessary power consumption. We aim to provide accurate and low-cost solar radiation estimation methods that are urgently needed. In this paper, a method of estimation of solar radiation inside CSGs based on a least mean squares (LMS) filter is proposed. The water required for tomato growth was also calculated based on the estimated solar radiation. Then, we compared the accuracy of this method to methods based on knowledge of astronomy and geometry for both solar radiation estimation and tomato water requirement. The results showed that the fitting function of estimation data based on the LMS filter and data collected from sensors inside the greenhouse was y = 0.7634x + 50.58, with the evaluation parameters of R2 = 0.8384, rRMSE = 23.1%, RMSE = 37.6 Wm−2, and MAE = 25.4 Wm−2. The fitting function of the water requirement calculated according to the proposed method and data collected from sensors inside the greenhouse was y = 0.8550x + 99.10 with the evaluation parameters of R2 = 0.9123, rRMSE = 8.8%, RMSE = 40.4 mL plant−1, and MAE = 31.5 mL plant−1. The results also indicate that this method is more effective. Additionally, its accuracy decreases as cloud cover increases. The performance is due to the LMS filter’s low pass characteristic that smooth the fluctuations. Furthermore, the LMS filter can be easily implemented on low cost processors. Therefore, the adoption of the proposed method is useful to improve the solar radiation sensing in CSGs with more accuracy and less expense.

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Section: Introductionmentioning

confidence: 99%

Estimation of Solar Radiation for Tomato Water Requirement Calculation in Chinese-Style Solar Greenhouses Based on Least Mean Squares Filter

Zhang

et al. 2019

Sensors

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“…For both algorithms, the parameter of step-size governs the convergence speed and the steady-state excess mean-square error. To better tradeoff the conflicting requirement of fast convergence rate and low misadjustment, various schemes for adjusting the step-size have been reported [4], [5], [6], [7],. To meet these conflicting requirements, the step size needs to be controlled.…”

Section: Introductionmentioning

confidence: 99%

Distribution free Variable Step Size Normalized Least Means Square Algorithm

Kosale¹

2015

IJRITCC

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Abstract-In this paper, we present a new approach to improve the acoustic echoes occurs in today's telecommunication system. We show that the new approach reduces the computational complexity; unlike old approaches like a LMS, NLMS and variable step size (VSS) algorithm, converges rate and misadjustment and computational complexity are biggest challenges for us. So new approach is known as DFVSS-NLMS algorithm and suitable candidate for a real world application.

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“…In the application of noise cancellation, least mean-square (LMS) algorithm is commonly used on adaptive filtering [5]. As traditional fixed step size of the standard LMS algorithm between convergence rate, time-varying systems tracking and stability imbalance for the selection of step size there is a big contradiction [6][7]. By improving the convergence properties of LMS algorithm, reducing the complexity, etc., made some improvements LMS adaptive filter algorithm, typically normalized variable step size LMS algorithm is NLMS [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Research on adaptive noise canceller of an improvement LMS algorithm

et al. 2011

2011 International Conference on Electronics, Communications and Control (ICECC)

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On the convergence of the LMS algorithm in adaptive filtering

Cited by 12 publications

References 2 publications

Estimation of Solar Radiation for Tomato Water Requirement Calculation in Chinese-Style Solar Greenhouses Based on Least Mean Squares Filter

Estimation of Solar Radiation for Tomato Water Requirement Calculation in Chinese-Style Solar Greenhouses Based on Least Mean Squares Filter

Distribution free Variable Step Size Normalized Least Means Square Algorithm

Research on adaptive noise canceller of an improvement LMS algorithm

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