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Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
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A novel fuzzy Wiener-based nonlinear modelling for engineering applications.

Tarek R Khalifa1, Ahmad M El-Nagar1, Mohamed A El-Brawany1

  • 1Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menof, 32852, Egypt.

ISA Transactions
|July 17, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new fuzzy Wiener structure for engineering system identification. It effectively models nonlinear systems with uncertainties and noise, outperforming existing methods with higher fitness and lower root mean square error.

Keywords:
Fuzzy Wiener modelInterval type-2 fuzzy systemsLyapunov stabilityNonlinear modelling

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Area of Science:

  • Engineering
  • Control Systems
  • Fuzzy Logic

Background:

  • Engineering systems often exhibit complex nonlinear dynamics.
  • Accurate system identification is crucial for control and analysis.
  • Existing models may struggle with uncertainties and noisy data.

Purpose of the Study:

  • To propose a novel fuzzy Wiener structure for identifying nonlinear engineering systems.
  • To develop a robust model capable of handling system uncertainties and noisy measurements.
  • To enhance the accuracy and performance of system identification techniques.

Main Methods:

  • A cascade structure combining a linear dynamic part (ARMA model) and a nonlinear static part (interval type-2 fuzzy Takagi-Sugeno-Kang system).
  • Utilizing interval type-2 fuzzy sets for rule antecedents and TSK-type systems for consequents.
  • Employing Lyapunov functions for parameter updates to guarantee model stability.

Main Results:

  • The proposed fuzzy Wiener structure successfully models nonlinear engineering applications.
  • The model demonstrates robustness in the presence of system uncertainties and noisy data.
  • Achieved superior performance with higher fitness (FIT) and lower root mean square error (RMSE) compared to existing schemes.

Conclusions:

  • The novel fuzzy Wiener structure provides an effective solution for identifying complex engineering systems.
  • The model's stability and accuracy are validated through simulation results.
  • This approach offers a significant improvement over conventional system identification methods.