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Related Concept Videos

State Space Representation01:27

State Space Representation

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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.
Consider an RLC circuit, a...
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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.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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Semi-supervised echo state network with temporal-spatial graph regularization for dynamic soft sensor modeling of

Ping Wang1, Yichao Yin1, Xiaogang Deng1

  • 1College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China.

ISA Transactions
|April 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a semi-supervised Echo State Network (ESN) using temporal-spatial graph regularization. This method enhances soft sensor models by utilizing both labeled and unlabeled data for improved prediction accuracy.

Keywords:
Dynamic processEcho state networkSemi-supervised modelSoft sensorTemporal-structure graph

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Area of Science:

  • Artificial Intelligence
  • Chemical Engineering
  • Data Science

Background:

  • Echo State Networks (ESNs) excel at nonlinear and dynamic modeling for industrial soft sensors.
  • Traditional ESNs are supervised, neglecting abundant unlabeled data.
  • This limitation hinders comprehensive model development.

Purpose of the Study:

  • To develop a semi-supervised ESN (SSESN) method for soft sensor modeling.
  • To leverage both labeled and unlabeled samples for enhanced model performance.
  • To introduce temporal-spatial graph regularization for improved generalization.

Main Methods:

  • Enhanced traditional ESN to create SSESN integrating labeled and unlabeled data.
  • Computed reservoir states at a high sampling rate for detailed dynamic information.
  • Modified SSESN output optimization with a temporal-spatial graph regularization term.

Main Results:

  • The proposed Temporal-Spatial Graph Regularized Semi-Supervised ESN (TSG-SSESN) model demonstrated superior performance.
  • TSG-SSESN achieved smoother models compared to basic ESN approaches.
  • The model exhibited enhanced generalization capabilities in soft sensor predictions.

Conclusions:

  • TSG-SSESN effectively utilizes all available data (labeled and unlabeled) for soft sensor construction.
  • The temporal-spatial graph regularization significantly improves model smoothness and predictive accuracy.
  • This approach offers a more robust and generalizable solution for industrial soft sensing applications.