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相关概念视频

State Space Representation01:27

State Space Representation

169
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
169
State Space to Transfer Function01:21

State Space to Transfer Function

177
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
177
Transfer Function to State Space01:23

Transfer Function to State Space

202
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
202
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

411
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
411
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.0K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

63
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
63

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相关实验视频

Updated: Jun 11, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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用二进制空间对动态网络的状态和参数估计进行贝叶斯优化.

Mohammad Alali1, Mahdi Imani1

  • 1Department of Electrical and Computer Engineering at Northeastern University.

Control Technology and Applications. Control Technology and Applications
|October 2, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种无梯度的方法,用于估计复杂布尔动态系统中的参数和状态. 该方法使用高斯过程和贝叶斯优化,证明对基因调节网络分析有效.

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科学领域:

  • 计算生物学 计算生物学
  • 系统生物学 系统生物学
  • 网络科学 网络科学

背景情况:

  • 部分观察布尔动态系统 (POBDS) 模型具有二进制状态的复杂网络.
  • 现有的参数估计方法通常是昂贵的基于梯度的计算技术,限制了可扩展性.

研究的目的:

  • 开发一种计算效率高,无梯度的方法,用于POBDS中的联合状态和参数估计.
  • 解决目前大规模网络分析方法的局限性.

主要方法:

  • 利用高斯过程来建模日志概率函数.
  • 采用贝叶斯优化来实现高效的参数空间搜索.
  • 集成了布尔和卡尔曼波器,用于联合状态估计.

主要成果:

  • 证明了拟议的无梯度方法的可扩展性和有效性.
  • 通过合成基因表达数据,成功地将该方法应用于基因调节网络.
  • 实现了模型参数和基因状态的准确联合估计.

结论:

  • 拟议的方法为POBDS提供了基于梯度的技术的可行和高效的替代方案.
  • 这种方法增强了复杂生物网络的分析,特别是基因调节网络.
  • 在大型系统中方便可靠的状态和参数估计.