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Electronics
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Nonlinear System Identification and Soft Sensor Design
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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 November 2024 | Viewed by 2401

Special Issue Editors

Prof. Dr. Luca Patanè

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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
Prof. Dr. Maria Gabriella Xibilia

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (3 papers)

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Research

22 pages, 7997 KiB  
Article
Beam Orbital Parameter Prediction Based on the Deployment of Cascaded Neural Networks at Edge Intelligence Acceleration Nodes
by Mingyang Hou, Yuhui Guo, Guijin Yang, Xuhui Yang, Zigeng Cao, Youxin Chen and Yuan He
Electronics 2024, 13(21), 4189; https://doi.org/10.3390/electronics13214189 - 25 Oct 2024
Viewed by 367
Abstract
During the beam current calibration process, accurate guidance of the beam current to the metal target is a challenging issue for proton accelerators. To address this challenge, we propose the use of beam orbital parameters combined with reinforcement learning algorithms to achieve automatic [...] Read more.
During the beam current calibration process, accurate guidance of the beam current to the metal target is a challenging issue for proton accelerators. To address this challenge, we propose the use of beam orbital parameters combined with reinforcement learning algorithms to achieve automatic beam calibration. This study introduces a system architecture that employs edge intelligent acceleration nodes based on deep learning acceleration techniques. We designed a system to predict BPM parameters using a cascaded backpropagation neural network (CBPNN) that is informed by the physical structure. This system serves as an environmental map for reinforcement learning, aiding beam current correction. The CBPNN was implemented on the acceleration node to hasten the forward inference process, leveraging sparsification, quantization algorithms, and pipelining techniques. Our experimental results demonstrated that the simulated inference speed reached 28 μs with FPGA hardware as the edge acceleration node, achieving forward inference speeds 35.66 and 12.66 times faster than those of the CPU and GPU. The energy efficiency ratio was 10.582 MOPS/W, which was 989 and 410 times that of the CPU and GPU, respectively. This confirms the designed architecture’s energy efficiency and low latency attributes. Full article
(This article belongs to the Special Issue Nonlinear System Identification and Soft Sensor Design)
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24 pages, 4997 KiB  
Article
Soft Sensors for Industrial Processes Using Multi-Step-Ahead Hankel Dynamic Mode Decomposition with Control
by Luca Patanè, Francesca Sapuppo and Maria Gabriella Xibilia
Electronics 2024, 13(15), 3047; https://doi.org/10.3390/electronics13153047 - 1 Aug 2024
Cited by 1 | Viewed by 659
Abstract
In this paper, a novel data-driven approach for the development of soft sensors (SSs) for multi-step-ahead prediction of industrial process variables is proposed. This method is based on the recent developments in Koopman operator theory and dynamic mode decomposition (DMD). It is derived [...] Read more.
In this paper, a novel data-driven approach for the development of soft sensors (SSs) for multi-step-ahead prediction of industrial process variables is proposed. This method is based on the recent developments in Koopman operator theory and dynamic mode decomposition (DMD). It is derived from Hankel DMD with control (HDMDc) to deal with highly nonlinear dynamics using augmented linear models, exploiting input and output regressors. The proposed multi-step-ahead HDMDc (MSA-HDMDc) is designed to perform multi-step prediction and capture complex dynamics with a linear approximation for a highly nonlinear system. This enables the construction of SSs capable of estimating the output of a process over a long period of time and/or using the developed SSs for model predictive control purposes. Hyperparameter tuning and model order reduction are specifically designed to perform multi-step-ahead predictions. Two real-world case studies consisting of a sulfur recovery unit and a debutanizer column, which are widely used as benchmarks in the SS field, are used to validate the proposed methodology. Data covering multiple system operating points are used for identification. The proposed MSA-HDMDc outperforms currently adopted methods in the SSs domain, such as autoregressive models with exogenous inputs and finite impulse response models, and proves to be robust to the variability of systems operating points. Full article
(This article belongs to the Special Issue Nonlinear System Identification and Soft Sensor Design)
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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 784
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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