Parameter optimization of nonlinear grey Bernoulli model using particle swarm optimization

Zhou, Jianzhong; Fang, Rengcun; Li, Yuhong; Zhang, Yongchuan; Peng, Bing

doi:10.1016/j.amc.2008.10.045

Cited by 84 publications

(46 citation statements)

References 13 publications

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“…However, statistical predictors may not provide satisfied results owing to the increasing complexity of real-world problems. Therefore, AI-based methods, such as neural networks [5][6][7][8][9], fuzzy systems [10,11], neural-fuzzy paradigms [12,13], and grey models [14][15][16][17][18][19], are proposed to increase the prediction accuracy.…”

Section: Grey Modelmentioning

confidence: 99%

Grey number prediction using the grey modification model with progression technique

Shih

Hsu

Yeh

et al. 2011

Applied Mathematical Modelling

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a b s t r a c tAlthough the grey model has been employed in various fields and demonstrated promising results, most applications focus on precise number predictions. However, owing to the increasing complexity of real-world problems, the grey number predictions will be more flexible and practical for grey model to describe the uncertain future tendency. For the purpose of establishing a grey model with grey number, this paper proposes a progression technique, which adopts different length series divided from the simulation data to produce a grey number prediction. Besides, this paper also modifies the algorithm of grey model, including altering the calculation of background value with an integration term and replacing the initial value of grey differential equation to the latest point, to enhance its accuracy. Two illustrative examples of numerical series and stock market are adopted for demonstrations. Results show that the proposed model can both catch the future tendency and reduce the loss of erroneous judgments.

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Section: Grey Modelmentioning

confidence: 99%

Grey number prediction using the grey modification model with progression technique

Shih

Hsu

Yeh

et al. 2011

Applied Mathematical Modelling

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show abstract

“…Unlike statistical approaches, this theory indirectly deals with original data through an accumulating generation operator (AGO), and tries to find the inherent structure in a system. In addition, assumptions concerning the statistical distribution are not required when using this approach, and the resulting flexibility means that it has been successfully applied in various fields [5][6][7][8].…”

Section: Introductionmentioning

confidence: 97%

A novel gray forecasting model based on the box plot for small manufacturing data sets

Chang

Huang

et al. 2015

Applied Mathematics and Computation

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“…; nÞ. For example, the original sequence [17] We can say that while x ð p q Þ ðkÞ À x ð p q Þ ðk À 1Þ is not a increasing sequence on kðk ¼ 2; 3; . .…”

Section: Introductionmentioning

confidence: 98%

Properties of the GM(1,1) with fractional order accumulation

Liu

Fang

et al. 2015

Applied Mathematics and Computation

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Parameter optimization of nonlinear grey Bernoulli model using particle swarm optimization

Cited by 84 publications

References 13 publications

Grey number prediction using the grey modification model with progression technique

Grey number prediction using the grey modification model with progression technique

A novel gray forecasting model based on the box plot for small manufacturing data sets

Properties of the GM(1,1) with fractional order accumulation

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