基于模拟退火算法的Blumberg模型参数估计:以肉鸡体重为例

Wahyudin Nur, Darmawati
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引用次数: 0

摘要

布隆伯格模型是逻辑模型之一。布隆伯格模型的优点是拐点的灵活性。布隆伯格模型被认为适合模拟活体器官的生长。在本文中,我们使用模拟退火算法估计Blumberg模型的参数。模拟退火算法是一种基于金属退火过程的启发式优化方法。所用数据为肉鸡日体重数据。所得模型与肉鸡日增重数据拟合。结果表明,冷却计划因子越接近1,误差越小。此外,我们必须仔细选择初始温度。初始温度的选择不当导致误差增大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parameter Estimation of The Blumberg Model Using Simulated Annealing Algorithm: Case Study of Broiler Body Weight
The Blumberg model is one of the logistic models. The advantage of the Blumberg model is the flexibility of the inflection point. The Blumberg model is believed to be suitable for modeling the growth of living organs. In this article, we estimate the parameters of the Blumberg model using simulated annealing algorithm. The simulated annealing algorithm is a heuristic optimization method based on the metal annealing process. The data used is Broiler  daily weight data. The model obtained fits the daily weight data of Broiler. Our results show that the closer the cooling schedule factor to 1, the smaller the error. In addition, we must carefully select the initial temperature. The selection of the initial temperature that is not suitable drives the error to enlarge.
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