Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS) algorithm, as a popular tool, achieves excellent performance for sparse system identification....
To address the sparse system identification problem under noisy input and non-Gaussian output measurement noise, two novel types of sparse bias-compensated normalized maximum correntropy criterion algorithms are developed, which are capable of elimin...
Power systems have an increasing demand for operational condition monitoring and safety control aspects. Low-frequency oscillation mode identification is one of the keys to maintain the safe and stable operation of power systems. To address the probl...
A general zero attraction (GZA) proportionate normalized maximum correntropy criterion (GZA-PNMCC) algorithm is devised and presented on the basis of the proportionate-type adaptive filter techniques and zero attracting theory to highly improve the s...
A soft parameter function penalized normalized maximum correntropy criterion (SPF-NMCC) algorithm is proposed for sparse system identification. The proposed SPF-NMCC algorithm is derived on the basis of the normalized adaptive filter theory, the maxi...
A novel adaptive filtering (AF) algorithm is proposed for group-sparse system identifications. In the devised algorithm, a novel mixed error criterion (MEC) with two-order logarithm error, p-order errors and group sparse constraint method is devised...
In view of the complex and changeable operating environment of pumped storage power stations and the noise and outliers in the modeling data, this study proposes a sparse robust least squares support vector machine (LSSVM) model based on the hybrid b...
In recent years, the rapid growth of computing technology has enabled identifying mathematical models for vibration systems using measurement data instead of domain knowledge. Within this category, the method Sparse Identification of Nonlinear Dynami...
Most works related to the identification of mathematical nonlinear systems suggest that such approaches can always be directly applied to any nonlinear system. This misconception is greatly discouraging when the obtained results are not expected. Thu...
Data-driven system identification procedures have recently enabled the reconstruction of governing differential equations from vibration signal recordings. In this contribution, the sparse identification of nonlinear dynamics is applied to structural...
Efficiently solving a system identification problem represents an important step in numerous important applications. In this framework, some of the most popular solutions rely on the Wiener filter, which is widely used in practice. Moreover, it also...
For sparse system identification, a memory-improved proportionate affine projection sign algorithm with a simplified, generalized correntropy induced metric (SGCI-M-IPAPSA) has good filtering performance. However, the SGCI-M-IPAPSA is based on a fixe...
The diffusion subband adaptive filtering (DSAF) algorithm has attracted much attention in recent years due to its decorrelation ability for colored input signals. In this paper, a modified DSAF algorithm using the symmetry maximum correntropy criteri...
This paper is concerned with the parameter estimation of non-linear discrete-time systems from noisy state measurements in the state-space form. A novel sparse Bayesian convex optimisation algorithm is proposed for the parameter estimation and predic...
The identification of dynamical systems from data is essential in control theory, enabling the creation of mathematical models that accurately represent the behavior of complex systems. However, real-world applications often present challenges such a...
As an essential part of the transmission system, gearboxes are considered as a major source of vibration. Signal identification of gear vibration is necessary for online monitoring of the mechanical systems. However, in engine-gearbox systems, the ig...
A sparsity-aware variable kernel width proportionate affine projection (AP) algorithm is devised for identifying sparse system in impulsive noise environments. For the devised algorithm, the symmetry maximum correntropy criterion (MCC) is employed to...
This paper proposes a dual adaptive Kalman filter to identify parameters of a dynamic system that may experience sudden damage by a dynamic excitation such as earthquake ground motion. While various filter techniques have been utilized to estimate sy...
Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for (a) describing the dynamics and (b) deriving discriminative fe...
This paper proposes a distributed estimation technique utilizing the diffusion least mean-square (LMS) algorithm, specifically designed for sparse systems in which many coefficients of the system are zeros. To efficiently utilize the sparse represent...
After a major seismic event, structural safety inspections by qualified experts are required prior to reoccupying a building and resuming operation. Such manual inspections are generally performed by teams of two or more experts and are time consumin...
Adaptive filtering plays a pivotal role in modern electronic information and communication systems, particularly in dynamic and complex environments. While traditional adaptive algorithms work well in many scenarios, they do not fully exploit the spa...
The rotor system is an important part of large-scale rotating machinery. Bearings, as a key component of the rotor system, play a vital role in the healthy operation of the rotor system. The bearings operate under harsh conditions such as high temper...
This paper focuses on the joint estimation of parameters and time-delays of the multiple-input single-output output-error systems. Since the time-delays are unknown, an effective identification model with a high dimensional and sparse parameter vecto...
An improved affine projection sign algorithm (APSA) was developed herein using a
Compressive Sensing (CS) is a transformative signal processing framework that enables sparse signal acquisition at rates below the Nyquist limit, offering substantial advantages in data efficiency and reconstruction accuracy. This survey explores the...
The expansion of renewables in modern power systems and the coordinated development of upstream and downstream industrial chains are promoting a shift on the utility side from traditional settlement by energy toward operation driven by data and model...
Voltage sag is one of the most serious problems in power quality. The occurrence of voltage sag will lead to a huge loss in the social economy and have a serious effect on people’s daily life. The identification of sag types is the basis for so...
The system identification of a ship dynamics model is crucial for the intelligent navigation and design of the ship’s controller. The fluid dynamic effect and the complicated geometry of the hull surface cause a nonlinear or asymmetrical behavior, an...
The presence of None-line-of-sight (NLOS) is one of the major challenging issues in time of arrival (TOA) based source localization, especially for the sparse anchor scenarios. Sparse anchors can reduce the system deployment cost, so this has become...
Urban railway track infrastructures often suffer from damage that affects their service performance due to a variety of factors. In this study, an unsupervised feature selection and damage identification method based on globally sparse probabilistic...
Exploring the design of beneficial nonlinear restoring force structures has become a highly popular topic due to their extensive applications in energy harvesting, actuation, energy absorption, robotics, etc. However, the current literature lacks a s...
Herein, two novel Physics Informed Neural Network (PINN) architectures are proposed for output-only system identification and input estimation of dynamic systems. Using merely sparse output-only measurements, the proposed PINNs architectures furnish...
A well-performing data-driven sparse sensor deployment strategy is critical for marine monitoring systems, as it enables the optimal reconstruction of marine physical quantities with fewer sensors. However, ocean data typically contain substantial am...
The present work aims at comparing different coupling coils by taking into account sources of uncertainty for static inductive power-transfer (SIPT) systems. Due to the maximum transmission efficiency for the SIPT system related to the mutual inducta...
Saturated nonlinear affine systems are widely encountered in many engineering fields. Currently, most control methods on saturated nonlinear affine systems are not specifically designed based on sparsity-based control methodologies, and they might re...
Human hand back skin texture (HBST) is often consistent for a person and distinctive from person to person. In this paper, we study the HBST pattern recognition problem with applications to personal identification and gender classification. A special...
Power grid parameter estimation involves the estimation of unknown parameters, such as the inertia and damping coefficients, from the observed dynamics. In this work, we present physics-informed machine learning algorithms for the power system parame...
The new coronavirus (COVID-19) is a disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 11 March 2020, the coronavirus outbreak has been labelled a global pandemic by the World Health Organization. In this conte...
The complexity of modern power grids, caused by integrating renewable energy sources, especially inverter-based resources, presents a significant challenge to grid operation and planning, since linear models are unable to capture the complex nonlinea...
Real-time and high-precision land cover classification is the foundation for efficient and quantitative research on grassland degradation using remote sensing techniques. In view of the shortcomings of manual surveying and satellite remote sensing, t...
Quorum sensing is a communication system by which bacteria use signal molecules to induce a physiological response. In natural marine environments, quorum sensing is suspected to occur in regions with high cell densities. Free-living bacteria, howeve...
Identifying inter-well connectivity is crucial for optimizing reservoir development and facilitating informed adjustments. While current engineering methods are effective, they are often prohibitively expensive due to the complex nature of reservoir...
Electroconvulsive therapy (ECT) represents an effective intervention for treatment-resistant depression (TRD). One priority of this research field is the clarification of ECT response mechanisms and the identification of biomarkers predicting its out...
This study proposes a comprehensive framework for the identification of nonlinear dynamics in Unmanned Aerial Vehicles (UAVs), integrating data-driven methodologies with theoretical modeling approaches. Two principal techniques are employed: Proporti...
In this paper, we present an effective method for analyzing patterns in the Russia–Ukraine war based on the Lanchester model. Due to the limited availability of information on combat powers of engaging forces, we utilize the loss of armored equ...
Accurate forecasting of ship encounter positions is crucial for preventing collisions at sea. This paper presents a framework for predicting a ship’s trajectory using a sparse Gaussian process. The proposed method effectively addresses the limi...
Postprandial plasma amino acid (AA) kinetics serve as essential indicators of digestive efficiency and systemic metabolic status in pigs. Traditional kinetic analysis relies on Non-Linear Least Squares (NLS) regression using compartmental models, yet...
Self-driving technology aims to minimize human error and improve safety, efficiency, and mobility through advanced autonomous driving algorithms. Among these, Model Predictive Control (MPC) is highly valued for its optimization capabilities and abili...
Developing accurate dynamic models for various systems is crucial for optimization, control, fault diagnosis, and prognosis. Recent advancements in information technologies and computing platforms enable the acquisition of input–output data fro...
of 3