New Approaches for System Identification Problems

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 3461

Special Issue Editors


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Guest Editor
Department of Telecommunications, University Politehnica of Bucharest, 1-3, Iuliu Maniu Blvd., 061071 Bucharest, Romania
Interests: adaptive filtering algorithms; multilinear systems; signal processing

E-Mail Website
Guest Editor
Department of Telecommunications, University Politehnica of Bucharest, 1-3, Iuliu Maniu Blvd., 061071 Bucharest, Romania
Interests: adaptive filters; acoustic echo cancellation; sparse systems
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Special Issue Information

Dear Colleagues,

System identification is the main framework in many important applications, e.g., acoustic echo cancellation, source separation, noise reduction, channel equalization, and machine learning. In numerous system identification problems, the unknown system can be modelled as a finite impulse response filter with a large number of coefficients, which raises additional challenges in terms of performance and complexity. Moreover, many of these systems may be time-variant; therefore, the impulse responses may change drastically over a short period of time. These aspects influence the overall performance of the global system. A natural approach is to exploit some specific characteristics of the systems to be identified, like intrinsic symmetric properties.

In this context, it would be of high interest to develop new techniques for the identification of such challenging systems. Research papers, as well as short communications and review articles comprising new approaches in this area are welcome in this Special Issue.  We would like to invite domestic and foreign experts to contribute with their research by employing the symmetry or asymmetry concepts in their methods and methodologies, including, but not limited to the areas listed below.

Submit your paper and select the Journal “Symmetry” and the Special Issue “New Approaches for System Identification Problems” via: MDPI submission system. Our papers will be published on a rolling basis and we will be pleased to receive your submission once you have finished it.

Lect. Dr. Laura-Maria Dogariu
Prof. Dr. Constantin Paleologu
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. Symmetry is an international peer-reviewed open access monthly 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

  • Adaptive filtering
  • Multilinear systems
  • Acoustic signal processing
  • Communication systems
  • Digital signal processing
  • Sparse systems
  • Tensor-based signal processing

Published Papers (2 papers)

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Research

11 pages, 2575 KiB  
Article
A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning System
by Xiaojia Wang, Cunjia Wang, Keyu Zhu and Xibin Zhao
Symmetry 2023, 15(1), 83; https://doi.org/10.3390/sym15010083 - 28 Dec 2022
Cited by 4 | Viewed by 1206
Abstract
In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical [...] Read more.
In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model—the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model’s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model—TSK-BLS, achieved significant improvements. Full article
(This article belongs to the Special Issue New Approaches for System Identification Problems)
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11 pages, 6531 KiB  
Communication
Stochastic Model for the LMS Algorithm with Symmetric/Antisymmetric Properties
by Augusto Cesar Becker, Eduardo Vinicius Kuhn, Marcos Vinicius Matsuo, Jacob Benesty, Constantin Paleologu, Laura-Maria Dogariu and Silviu Ciochină
Symmetry 2022, 14(9), 1908; https://doi.org/10.3390/sym14091908 - 12 Sep 2022
Viewed by 1227
Abstract
This paper presents a stochastic model for the least-mean-square algorithm with symmetric/antisymmetric properties (LMS-SAS), operating in a system identification setup with Gaussian input data. Specifically, model expressions are derived to describe the mean weight behavior of the (global and virtual) adaptive filters, learning [...] Read more.
This paper presents a stochastic model for the least-mean-square algorithm with symmetric/antisymmetric properties (LMS-SAS), operating in a system identification setup with Gaussian input data. Specifically, model expressions are derived to describe the mean weight behavior of the (global and virtual) adaptive filters, learning curves, and evolution of some correlation-like matrices, which allow predicting the algorithm behavior. Simulation results are shown and discussed, confirming the accuracy of the proposed model for both transient and steady-state phases. Full article
(This article belongs to the Special Issue New Approaches for System Identification Problems)
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