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Related Concept Videos

State Space Representation01:27

State Space Representation

476
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...
476
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Transfer Function to State Space01:23

Transfer Function to State Space

703
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 RLC...
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State Space to Transfer Function01:21

State Space to Transfer Function

508
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:
508
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

301
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

446
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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Updated: Dec 29, 2025

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
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A Multidimensional Array Representation of State-Transition Model Dynamics.

Eline M Krijkamp1, Fernando Alarid-Escudero2, Eva A Enns3

  • 1Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|January 29, 2020
PubMed
Summary

This study introduces a new method for cohort state-transition models (cSTMs) to capture detailed health state transitions. The approach uses a multidimensional array to provide richer data than traditional cohort traces for better model calibration.

Keywords:
R projectcost-effectiveness analysisdecision modelinghealth economicsmatricesmultidimensional arraysstate-transition modelstensorstransition dynamicstransition rewards

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Area of Science:

  • Health economics
  • Mathematical modeling
  • Epidemiology

Background:

  • Cohort state-transition models (cSTMs) are vital for cost-effectiveness analyses.
  • The standard outcome, the cohort trace, aggregates health state occupancy over time.
  • This aggregation omits crucial transition dynamics information.

Purpose of the Study:

  • To propose an alternative method for computing and storing cSTM outcomes.
  • To capture both state occupancy and transition dynamics.
  • To enhance model calibration and validation capabilities.

Main Methods:

  • Developed an approach using a multidimensional array to store cSTM outcomes.
  • This array captures detailed transition dynamics alongside state occupancy.
  • An example implementation in R is provided.

Main Results:

  • The multidimensional array approach recovers both state occupancy and transition dynamics.
  • This method offers advantages over the traditional aggregated cohort trace.
  • Demonstrated potential applications for epidemiological outcomes and model calibration.

Conclusions:

  • The proposed multidimensional array method provides a more comprehensive outcome for cSTMs.
  • This approach facilitates the recovery of detailed transition dynamics.
  • Enables advanced applications in health economics and epidemiological research.