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The Improvement of Discrete GM(1,1) Prediction Model and its Solution Arithmetic

机译:分离GM(1,1)预测模型及其解决方案算术的改进

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The GM(1,1) model assume the sequence is analogous to exponential law. Great error appears when it is used to simulate many non-lineal sequences. The paper proves that the growth rates of the simulated value of the GM(1,1) model and the discrete GM(1,1) model are both fixed value. If the growth rates of the primary sequence are equate, the fitted value deriving from the discrete GM(1,1) model the same as the primary sequence. The paper improves the discrete GM(1,1) model. Using the optimization method, the paper studies the initial value. The paper puts forward the solution arithmetic to the optimization and proves the efficiency of the arithmetic by means of a example. The research indicates the discrete grey extension model can greatly improve the simulated intensity and it can solve the simulated of the non-lineal non-negative sequence.
机译:GM(1,1)模型假设序列类似于指数律法。当它用于模拟许多非线性序列时,会出现很大的错误。本文证明了GM(1,1)模型和离散GM(1,1)模型的模拟值的生长速率均为固定值。如果主序列的生长速率等同于,则从离散GM(1,1)模型导出的拟合值与主要序列相同。本文改善了离散的GM(1,1)模型。使用优化方法,论文研究了初始值。本文将解决方案算法转发到优化,并通过示例证明算术的效率。该研究表明离散灰度扩展模型可以大大提高模拟强度,并且可以解决非线性非负序列的模拟。

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