Nonlinear System Identification and Soft Sensor Design

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 341

Special Issue Editors


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Guest Editor
Department of Engineering, University of Messina, Contrada di Dio, S. Agata, 98166 Messina, Italy
Interests: nonlinear systems modeling and control; bio-robotics; locomotion control, spiking neural networks, insect-inspired control systems; system identification and soft sensor development
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Engineering, University of Messina, Contrada Di Dio, Vill. S. Agata , 98166 Messina, Italy
Interests: system identification; soft sensors; soft computing; machine learning; neural networks; nonlinear control; complex systems; industrial automation; process monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, nonlinear system identification and soft sensor design have became more significant in various scientific and industrial domains. Nonlinear systems are ubiquitous, found in areas such as Industry 4.0, IoT, biology, economics, and environmental science. The accurate modelling and control of these systems are essential for optimizing processes, enhancing performance, and ensuring safety. Additionally, soft sensors, which are data-driven models capable of estimating unmeasured process variables, play a crucial role in the real-time monitoring and control of complex systems, providing cost-effective alternatives to traditional sensors. Soft sensors are usually designed to exploit nonlinear system methodologies and machine learning/deep learning approaches.

This Special Issue aims to explore the latest advancements, challenges, and applications in the interdisciplinary domain of nonlinear system identification and soft sensor design. By collating researchers from different fields, we aim to foster collaboration and knowledge exchange in this rapidly evolving area.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Feature extraction;
  • Outlier detection;
  • Data selection;
  • Big and small datasets;
  • System identification;
  • Linear and nonlinear models;
  • Deep learning techniques;
  • Optimization strategies;
  • Recurrent neural networks;
  • Reservoir computing;
  • Bio-inspired learning techniques;
  • Model validation;
  • Soft sensor maintenance;
  • Transfer learning;
  • Model interpretability;
  • Sparse modeling;
  • Neuromorphic computing;
  • Soft sensors for time-varying systems;
  • Industrial applications of soft sensors;
  • Fault detection.

Prof. Dr. Luca Patanè
Prof. Dr. Maria Gabriella Xibilia
Guest Editors

Manuscript Submission Information

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Keywords

  • system identification big and small datasets
  • soft sensor maintenance
  • transfer learning
  • model interpretability
  • industrial applications of soft sensors
  • soft sensors for predictive maintenance

Published Papers (1 paper)

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Research

17 pages, 5650 KiB  
Article
Concurrent Learning-Based Two-Stage Predefined-Time System Identification
by Bojun Liu, Zhanpeng Zhang and Yingmin Yi
Electronics 2024, 13(8), 1460; https://doi.org/10.3390/electronics13081460 - 12 Apr 2024
Viewed by 111
Abstract
This paper proposes a novel two-stage predefined-time system identification algorithm for uncertain nonlinear systems based on concurrent learning. The main feature of the algorithm is that the convergence time of estimation error is an exact predefined parameter, which can be known and adjusted [...] Read more.
This paper proposes a novel two-stage predefined-time system identification algorithm for uncertain nonlinear systems based on concurrent learning. The main feature of the algorithm is that the convergence time of estimation error is an exact predefined parameter, which can be known and adjusted directly by users. Historic identification data are stored in the first stage to guarantee that a finite-rank condition is satisfied. In the second stage, the estimation error converges to zero for linearly parameterized uncertain systems, or it is regulated into the neighborhood of zero for unknown systems modeled by neural networks. The identification algorithm takes effect without the restrictive requirement of the persistent excitation condition. Simulation examples verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Nonlinear System Identification and Soft Sensor Design)
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