An Approach to Improve Estimation Performance of GM(1,1) Model

Hiseh, Cheng-Hsiung; Huang, Ren-Hsien; Feng, Ting-Yu

doi:10.20965/jaciii.2008.p0249

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…where N is the number of samples used in calculation, and the grey model requires at least 4 samples. As increasing the number of samples does not necessarily improve the prediction accuracy [37], N is set to 4 when considering the node energy consumption. The following is the step of estimatinĝ UðkÞ andVðkÞ using the grey model of the fractional-order operator.…”

Section: Modeling Of Clock Offset Based On Fractional-ordermentioning

confidence: 99%

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Adaptive Fractional-Order Operator Clock Synchronization Algorithm Based on Grey Prediction

et al. 2020

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As a result of the influence of clock drift and uncertainty delay in synchronous message transmission, the clock synchronization model based on statistical distribution cannot accurately describe clock deviation. This model also requires a large number of timestamp samples that cause a storage occupation issue for wireless sensor nodes with limited resources. The modeling method based on grey prediction has advantages of low sample demand and simple modeling process. However, the accuracy of the existing clock synchronization models needs to be improved. Based on the grey prediction theory, this paper proposes an adaptive fractional-order operator clock synchronization algorithm considering uncertainty delay. First, based on the clock model and clock offset model, the frequency offset between nodes is optimized by taking the mean on the clock frequencies. Second, a grey prediction algorithm based on a fractional-order operator is proposed by estimating the uncertainty delay in message transmission to obtain the clock offset. Finally, the order of the fractional-order accumulation is adjusted adaptively in the grey prediction model according to the collected timestamp sample values so that the estimation of the uncertainty delay is more accurate, thereby improving the accuracy of the clock offset. Compared with the first-order accumulative grey prediction clock synchronization algorithms and timing-sync protocol for sensor networks, the proposed scheme improved the synchronization accuracy by 29.18% and 44.01%, respectively, and reduced the variance of the clock offset by 48.66% and 64.89%. Thus, the proposed algorithm is characterized by improved stability.

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Section: Modeling Of Clock Offset Based On Fractional-ordermentioning

confidence: 99%

“…From [37], when r ∈ R + , X ðrÞ = ðx ðrÞ ð1Þ, x ðrÞ ð2Þ, ⋯, x ðrÞ ðnÞÞ is the r-order accumulation that generates operators of X ð0Þ , where…”

Section: Modeling Of Clock Offset Based On Fractional-ordermentioning

confidence: 99%

Adaptive Fractional-Order Operator Clock Synchronization Algorithm Based on Grey Prediction

et al. 2020

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“…Grey system theory is an interdisciplinary scientific area that was first proposed [14]. As superiority to statistical models, grey model requires only limited amount of data to evaluate unknown systems.…”

Section: Introductionmentioning

confidence: 99%

Time Series Forecasting with Stochastic Markov Models Based on Fuzzy Set and Grey Theory

Wong

2015

AMM

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The stochastic Markov model is combined with fuzzy set concept and grey system for improving forecasting performance. The data for model test is obtained from ACI including Hong Kong, Beijing, Taoyuan, Incheon and Narita international airport. The empirical results show that fuzzy Markov model has better predictive performance with the data with trend and intercept. For the data with random walk, grey Markov model performs better. The paper also examines the effects of transition state and length of interval on the forecasting performance with the result. Keywords: Markov model, Grey Markov model, Air cargo flow, ARIMA, MAPE.

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Using improved grey forecasting models to forecast the output of opto-electronics industry

Hsu

2011

Expert Systems with Applications

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An Approach to Improve Estimation Performance of GM(1,1) Model

Cited by 3 publications

References 3 publications

Adaptive Fractional-Order Operator Clock Synchronization Algorithm Based on Grey Prediction

Adaptive Fractional-Order Operator Clock Synchronization Algorithm Based on Grey Prediction

Time Series Forecasting with Stochastic Markov Models Based on Fuzzy Set and Grey Theory

Using improved grey forecasting models to forecast the output of opto-electronics industry

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