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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

81
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
81
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
65
Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
115
Stereotype Content Model02:16

Stereotype Content Model

14.8K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Response Surface Methodology01:16

Response Surface Methodology

185
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
185

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使用结构自适应智能灰色模型的预测方法.

Flavian Emmanuel Sapnken1,2, Jean Gaston Tamba1,2

  • 1Laboratory of Technologies and Applied Science, PO Box 8698, IUT Douala, Douala, Cameroon.

MethodsX
|July 10, 2023
PubMed
概括

一个新的结构自适应智能灰色模型 (SAIGM) 改善了石油产品消费预测. 这种可适应的模型提高了能源规划和储备管理的预测准确性.

科学领域:

  • 能源经济学 能源经济学
  • 预测科学 预测科学
  • 数学建模的数学建模

背景情况:

  • 准确的中长期石油产品 (PP) 消费预测对于战略储备管理和能源规划至关重要.
  • 传统的灰色模型对各种预测场景的适应性和准确性有局限性.

研究的目的:

  • 开发一种新的结构自适应智能灰色模型 (SAIGM),用于增强能源预测.
  • 提高石油产品消费预测的准确性和灵活性.

主要方法:

  • 开发了一种新的时间响应功能,以解决传统灰色模型的弱点.
  • 使用SAIGM计算了最佳参数值,以提高适应性.
  • 使用合成代数序列和喀麦隆的现实石油消费数据验证了SAIGM模型.

主要成果:

  • SAIGM 实现了高精度,其 RMSE 值为 3.10 和 1.54% MAPE.
  • 与现有的智能灰色系统相比,该模型表现出卓越的性能.
  • SAIGM有效地从数据中提取基本模式,而不需要输入属性确定或数据预处理.

结论:

  • SAIGM是一个强大而灵活的石油产品消费预测工具.
关键词:
灰色预测模型的预测模型建模知识的知识建模.参数化的参数化这就是SAIGM模型.结构自适应智能灰色模型 (SAIGM)结构灵活性 结构灵活性

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  • 该模型为智能灰色模型提供了增强的预测能力,可适应各种数据规格.
  • SAIGM为跟踪和预测能源需求增长提供了有效的方法,如喀麦隆的PP消费数据所示.