Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

417
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
417
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

543
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
543
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

253
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
253
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

330
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
330
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

247
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...
247
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

516
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
516

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A robust data-driven system identification approach with applications to reliability analysis of DC motor systems.

ISA transactions·2026
Same author

NSUN2/ALYREF-mediated RNA m5c modification promotes anoikis resistance of prostate cancer through activating autophagy.

Oncogene·2026
Same author

Quantitative Assessment of Upper Limb Multi-Modal Feature Fusion Under Task-Oriented Movement.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Synthesis of FeOOH/Al<sub>2</sub>O<sub>3</sub> Composites with Excellent Adsorption Performance and Regenerability for Phosphate Removal from Wastewater.

Molecules (Basel, Switzerland)·2025
Same author

Multi-omic analysis constructs ferroptosis subtypes and risk signature and reveals that PEBP1 is an important tumor suppressor in kidney cancer.

Human cell·2025
Same author

Efficient removal of Sunset Yellow by silicon-phosphorus modified alumina: Adsorption mechanisms and regeneration study.

Journal of hazardous materials·2025

Related Experiment Video

Updated: Jan 24, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

486

Quantized Sampled-Data Control for T-S Fuzzy System Using Discontinuous LKF Approach.

Shenquan Wang1, Shuaiqi Chen1, Wenchengyu Ji1

  • 1College of Electrical and Electronic Engineering Changchun University of Technology, Changchun, China.

Frontiers in Neuroscience
|May 21, 2019
PubMed
Summary

This study enhances stability analysis for sampled-data Takagi-Sugeno (T-S) fuzzy systems with state quantization. New methods achieve less conservative stability conditions and improved controllers, outperforming existing results.

Keywords:
T-S fuzzy systemsdiscontinuous LKF approachquantizationsampled-data systemstabilization

More Related Videos

Preparation of Synaptoneurosomes from Mouse Cortex using a Discontinuous Percoll-Sucrose Density Gradient
08:30

Preparation of Synaptoneurosomes from Mouse Cortex using a Discontinuous Percoll-Sucrose Density Gradient

Published on: September 17, 2011

32.8K
An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.7K

Related Experiment Videos

Last Updated: Jan 24, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

486
Preparation of Synaptoneurosomes from Mouse Cortex using a Discontinuous Percoll-Sucrose Density Gradient
08:30

Preparation of Synaptoneurosomes from Mouse Cortex using a Discontinuous Percoll-Sucrose Density Gradient

Published on: September 17, 2011

32.8K
An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.7K

Area of Science:

  • Control Systems Engineering
  • Fuzzy Logic Systems
  • Nonlinear Control Theory

Background:

  • Sampled-data Takagi-Sugeno (T-S) fuzzy systems are widely used in control applications.
  • State quantization introduces uncertainty and challenges stability analysis.
  • Existing methods for analyzing such systems can be conservative.

Purpose of the Study:

  • To investigate the stability of sampled-data T-S fuzzy systems with state quantization.
  • To develop a less conservative stability condition and design a robust controller.
  • To extend the findings to systems without state quantization.

Main Methods:

  • Utilized a discontinuous Lyapunov-Krasoskii functional (LKF) approach.
  • Employed free-matrix-based integral inequality bounds processing techniques.
  • Designed controllers for both quantized and unquantized state systems.

Main Results:

  • Obtained a stability condition with reduced conservativeness for quantized systems.
  • Successfully designed controllers for sampled-data T-S fuzzy systems with quantized states.
  • Demonstrated superior performance in terms of maximum sampling intervals compared to existing results.

Conclusions:

  • The proposed methods provide effective stability analysis and controller design for sampled-data T-S fuzzy systems with state quantization.
  • The findings are applicable to both quantized and unquantized state scenarios.
  • The study advances the understanding and control of complex fuzzy systems.