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Related Concept Videos

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:
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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...
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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

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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...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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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...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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...
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Bayesian Optimization for State and Parameter Estimation of Dynamic Networks with Binary Space.

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
Summary
This summary is machine-generated.

This study introduces a gradient-free method for estimating parameters and states in complex Boolean dynamical systems. The approach uses Gaussian processes and Bayesian optimization, proving effective for gene regulatory network analysis.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Network Science

Background:

  • Partially Observed Boolean Dynamical Systems (POBDS) model complex networks with binary states.
  • Existing parameter estimation methods are often computationally expensive gradient-based techniques, limiting scalability.

Purpose of the Study:

  • To develop a computationally efficient, gradient-free method for joint state and parameter estimation in POBDS.
  • To address the limitations of current methods for large-scale network analysis.

Main Methods:

  • Utilized Gaussian processes to model the log-likelihood function.
  • Employed Bayesian optimization for efficient parameter space search.
  • Integrated the Boolean Kalman filter for joint state estimation.

Main Results:

  • Demonstrated the scalability and effectiveness of the proposed gradient-free approach.
  • Successfully applied the method to gene regulatory networks using synthetic gene-expression data.
  • Achieved accurate joint estimation of model parameters and gene states.

Conclusions:

  • The proposed method offers a viable and efficient alternative to gradient-based techniques for POBDS.
  • This approach enhances the analysis of complex biological networks, particularly gene regulatory networks.
  • Facilitates robust state and parameter estimation in large-scale systems.