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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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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.
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Nonlinear dynamic transfer partial least squares for domain adaptive regression.

Zhijun Zhao1, Gaowei Yan2, Mifeng Ren1

  • 1College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China.

ISA Transactions
|August 14, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel nonlinear dynamic transfer soft sensor algorithm to combat model degradation in changing conditions. The method effectively handles complex data, improving soft sensor performance across diverse applications.

Keywords:
Domain adaptive regressionDynamic partial least squaresSoft sensorTransfer learning

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Area of Science:

  • Chemical Engineering
  • Process Control
  • Data Science

Background:

  • Soft sensor models often degrade under evolving working conditions.
  • Dynamic, nonlinear, and multimodal data present challenges for traditional soft sensors.

Purpose of the Study:

  • To develop a robust nonlinear dynamic transfer soft sensor algorithm.
  • To address model degradation and accommodate complex data characteristics.

Main Methods:

  • Time-delay data augmentation for capturing system dynamics.
  • Latent space projection with distribution alignment and first-order difference regularization.
  • Laplace regularization for geometric structure preservation in nonlinear regression.
  • Partial least squares optimization framework with Bayesian hyperparameter tuning.

Main Results:

  • The proposed algorithm demonstrated effectiveness on three public datasets.
  • Successfully addressed data distribution disparities and preserved geometric structures.
  • Improved soft sensor performance under changing working conditions.

Conclusions:

  • The nonlinear dynamic transfer soft sensor algorithm offers a robust solution for industrial applications.
  • The method enhances the adaptability and reliability of soft sensors in dynamic environments.