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Article
Engineering, Electrical & Electronic
Yuxiang Li et al.
Summary: To address the issue of destroying the location dependence of EEG signals, a support matrix machine (SMM) method is proposed to incorporate the location information into the input data matrix for classification. The article investigates an auto-correlation function based sparse support matrix machine (ACF-SSMM) algorithm to optimize and classify EEG fatigue signals.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Automation & Control Systems
Wentao Liu et al.
Summary: This paper investigates the parameter estimation algorithms of a finite impulse response system with colored noise. A novel gradient-based algorithm is developed using the cost function of the continuous mixed p-norm (CMPN) to suppress the negative effects of the colored noises. The algorithm combines p-norms for 1 <= p <= 2, controlling the proportions of error norms and generating an adjustable gain to adapt the data quality. Additionally, a CMPN multi-innovation gradient recursive algorithm is derived to enhance the convergence rate by expanding the innovation scalar to the innovation vector. Two examples are provided to demonstrate the effectiveness of the proposed algorithms.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Automation & Control Systems
Jian Pan et al.
Summary: This paper investigates parameter estimation methods for the nonlinear exponential autoregressive model. A forgetting factor gradient parameter estimation algorithm is proposed to improve estimation accuracy. Additionally, a forgetting factor multi-innovation stochastic gradient algorithm is derived using the multi-innovation theory to further enhance identification accuracy. The effectiveness of these algorithms is validated through a simulation example.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Automation & Control Systems
Ning Xu et al.
Summary: This article focuses on the identification of time-varying systems. Unlike conventional polynomial approximation approaches, the changing laws of the time-varying parameters are taken into account to establish the identification model. The concept of the invariant matrix is introduced to characterize the time-varying parameters and establish the state-space model. Two state estimation algorithms, stacked and detached, are proposed to estimate the time-varying parameters and enhance computational efficiency. Numerical simulation examples are provided to demonstrate the effectiveness of the proposed algorithms.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Jian Pan et al.
Summary: This paper proposes a novel single-phase nine-level switched-capacitor inverter with the ability of quadruple-boost and reducing the component counts. The proposed topology uses only one DC source, nine switches, two diodes, and two switched capacitors to achieve nine-level output. A simple logic-gate-based pulse width modulation scheme is developed for controlling the switches. Detailed analysis and simulations in Matlab/Simulink R2021b demonstrate the superiority and feasibility of the proposed converter.
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Yuan Cao et al.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2023)
Article
Physics, Condensed Matter
Haijun Wang et al.
Summary: This paper introduces a new 3D sub-quadratic Lorenz-like system, which shows the existence of two pairs of heteroclinic orbits connecting nontrivial equilibria and the origin. This differs from the published literature, which only discusses the connections to the unstable origin and a pair of stable equilibria. The paper also highlights the need for further exploration of the system's dynamics, such as Hopf bifurcation, invariant algebraic surfaces, ultimate boundedness, and singularly degenerate heteroclinic cycles.
EUROPEAN PHYSICAL JOURNAL B
(2023)
Article
Computer Science, Artificial Intelligence
Yang Li et al.
Summary: With the development of artificial intelligence and the broad application of sensors, human activity recognition (HAR) technologies based on noninvasive environmental sensors have received extensive attention and have shown great application value. This study proposes a method for single user's daily behavior recognition in multitenant smart home scenarios, which can adaptively constrain sensor noise and improve recognition accuracy.
INFORMATION FUSION
(2023)
Article
Automation & Control Systems
Chong Hu et al.
Summary: This paper investigates the parameter identification issue for the fractional-order input nonlinear output error autoregressive (IN-OEAR) model. By expressing the output form of the system as a linear combination of unknown parameters through key term separation, the problem of large computation of redundant parameter estimation is avoided. The fractional-order IN-OEAR model is decomposed into two sub-models with a smaller number of parameters using the hierarchical identification principle. Recursive least squares and gradient stochastic sub-algorithms are proposed for parameter and fractional-order estimation, respectively. A two-stage multi-innovation least recursive algorithm is proposed to achieve more accurate parameter estimates based on the multi-innovation identification theory. Numerical simulation results demonstrate the effectiveness of the proposed methods.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Jian Pan et al.
Summary: In this paper, the attitude control of a quadrotor system under external disturbance is investigated. A novel adaptive sliding mode control based on the linear extended state observer is proposed to estimate the disturbances. An adaptive switching algorithm is developed to dynamically adjust the switching gain in real time, enabling compensation of the disturbance estimation error. The stability of the system is proven using Lyapunov theory, and simulation and experimental results demonstrate the effectiveness of the proposed control method.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Automation & Control Systems
Ling Xu
Summary: This paper considers the problem of estimating parameters in nonlinear models based on response data. It proposes a nonlinear dynamical optimization scheme to obtain parameter estimates by constructing a gradient criterion function and deriving a gradient recursion algorithm. To overcome the difficulty of determining the step-size in the algorithm, a trying method and a numerical approach are proposed. Furthermore, stochastic gradient estimation methods, including a recursive step-size method and a multi-innovation method using dynamical window data, are presented to enhance estimation accuracy.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Automation & Control Systems
Feng Ding et al.
Summary: This paper proposes a filtered auxiliary model generalized extended stochastic gradient identification method, which can be applied to linear and nonlinear multivariable stochastic systems with colored noises.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Multidisciplinary Sciences
Haijun Wang et al.
Summary: This paper introduces a new 3D cubic Lorenz-like system by adding nonlinear terms to the second equation, which exhibits globally exponentially asymptotical stability of parabolic type equilibria and the existence of heteroclinic orbits.
SCIENTIFIC REPORTS
(2023)
Article
Automation & Control Systems
Jie Hou et al.
Summary: In this article, a consistent subspace identification method (SIM) is proposed for block-oriented errors-in-variables Hammerstein systems. The existing SIMs using parity subspace based on noisy measurements may result in biased parameter estimates. We propose a scheme for consistent system parameter estimation, which estimates the noise-free Hankel matrix using available noisy measurements and noise variances. The effectiveness and merits of the proposed method are supported by two simulation examples.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Energy & Fuels
Dongqing Wang et al.
Summary: This paper introduces a hierarchical adaptive extended Kalman filter (HAEKF) algorithm for state of charge (SOC) estimation in battery management system (BMS). The algorithm utilizes an adaptive EKF algorithm with online updating of the Sage-Husa estimator for improved SOC estimation. The hierarchical identification principle is used to decompose the circuit state equation model into two fictitious submodels with different sampling rates, allowing for fast and slow dynamic estimation using the HAEKF algorithm. Experimental results under the urban dynamometer driving schedule (UDDS) test condition confirm the high accuracy, low computational cost, and strong robustness of the HAEKF algorithm under left biased measurement noise variance.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Mathematics, Interdisciplinary Applications
Ausif Padder et al.
Summary: In this study, the Caputo fractional-order derivative is used to perform a dynamical analysis of a generalized tumor model. The results show that the Caputo fractional-order derivative provides a more accurate description of the tumor growth dynamics compared to classical integer-order derivatives. The study also highlights the potential of the Caputo fractional-order derivative as a valuable tool in biomedical research.
FRACTAL AND FRACTIONAL
(2023)
Article
Automation & Control Systems
Jie Hou et al.
Summary: In this paper, a bias-correction least-squares (LS) algorithm is proposed for identifying block-oriented errors-in-variables nonlinear Hammerstein (EIV-Hammerstein) systems. The estimation bias caused by additive white noises in the EIV-Hammerstein system is addressed and a bias-estimation scheme based on noisy measurements is proposed for consistent parameter estimation. A specific algorithm based on minimizing the output prediction error is given to estimate the unknown noise variances. The effectiveness of the proposed method is demonstrated through simulation and experimental verification using a wireless power transfer system.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Computer Science, Information Systems
Yongqiang Zheng et al.
Summary: In this study, an adaptive neural decision tree is investigated for the recognition of electroencephalogram (EEG) emotion signal. The method intelligently selects network structure and overcomes the lack of position information in the input signal by converting it into a two-dimensional matrix signal with added channel position information. The use of adaptive moment estimation algorithm and exploration-exploitation trade-off reinforcement learning method enables the algorithm to automatically search for optimized parameters and explore tree architectures for global optimal network structure. Experimental results on DEAP datasets demonstrate the effectiveness of the proposed method compared to the traditional decision tree method.
INFORMATION SCIENCES
(2023)
Article
Mathematics, Applied
Feng Ding
Summary: Least squares is an important method used for solving linear fitting and quadratic optimization problems. This paper explores the properties of least squares methods and multi-innovation least squares methods, and demonstrates important contributions in the area of system identification such as auxiliary model identification, multi-innovation identification theory, hierarchical identification principle, coupling identification concept, and filtering identification idea. The results of least squares and multi-innovation least squares algorithms for linear regressive systems with white noises can be extended to systems with colored noises.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Physics, Multidisciplinary
H. Wang et al.
Summary: By revisiting a four-dimensional chaotic system, this paper uncovers hidden dynamical behaviors that were not previously reported, including the distribution and stability of equilibrium points, bifurcations, coexistence of different cycles, and the existence of attractive sets and heteroclinic orbits. The paper presents innovative results in the formulation of hyperchaos, the identification of attractive sets, and the proof of symmetric heteroclinic orbits. These findings improve and complement the known results and have implications for real-world applications.
INDIAN JOURNAL OF PHYSICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Yuan Cao et al.
Summary: This paper proposes a virtual coupling scheme based on the local leader-follower method. The controller based on artificial potential field with variable parameter mixed is applied to carry out the collaborative and anti-collision control for the virtual coupled train system. The results show that the variable parameter artificial potential field controller can significantly reduce the average error of the stop compared to the traditional controller.
FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY
(2022)
Article
Engineering, Electrical & Electronic
Huan Xu et al.
Summary: This paper focuses on estimating parameters and time-delay in nonlinear time-series modeling. A new algorithm is proposed that enhances data utilization by using redundancy and multi-innovation theory. Simulation results demonstrate that the estimation accuracy of the proposed algorithm is significantly higher than that of traditional algorithms.
IEEE SIGNAL PROCESSING LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Xiao Zhang et al.
Summary: This study builds on previous work in filter design and proposes a solution to the problem of correlated noise disturbance by introducing a linear prefilter to obtain unbiased estimate of the filter weight. Moreover, compared to integer-order-based adaptive algorithms, the fractional-order-based algorithms show better performance.
IEEE SIGNAL PROCESSING LETTERS
(2022)
Article
Automation & Control Systems
Shah Zeb et al.
Summary: This article investigates the channel modeling of mmWave-B5G systems in indoor factory environments. A 3-D stochastic channel model is implemented using the time-cluster spatial-lobe technique to reflect realistic indoor conditions. The performance analysis of the system is presented based on the generated channel impulse response.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Yamin Fan et al.
Summary: This article proposes a new approach for parameter estimation of an input nonlinear controlled autoregressive moving average system by introducing a switching function and utilizing multi-innovation identification theory to improve accuracy. Simulation results demonstrate the effectiveness of the new algorithms and show higher identification accuracy compared to other algorithms.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Jimei Li et al.
Summary: In this article, a nonlinear optimization method for fitting Gaussian functions in artificial intelligence applications is proposed. The authors present iterative algorithms using gradient search and Newton search to identify Gaussian functions, as well as algorithms specifically designed for noisy Gaussian functions. Simulation results demonstrate that the proposed method and gradient-based algorithms are effective in fitting noisy Gaussian functions.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Ya Gu et al.
Summary: This article introduces a state-space model with time-delay to map the relationship between known input-output data for discrete systems, and proposes a model identification algorithm and parameter estimation algorithm, followed by the introduction of U-model-based control in the design of control systems. The derived results are validated through computational experiments.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Ling Xu
Summary: This paper addresses the modelling problem of dynamical systems and aims to develop highly accurate modelling approaches. Through experiments and parameter decomposition, two separable identification models are constructed and a separable Newton recursive parameter estimation approach is developed, resulting in more accurate parameter estimates.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Automation & Control Systems
Jing Chen et al.
Summary: The study introduces an enhanced flexible least squares algorithm for time-varying parameter systems, which minimizes residual and dynamic errors to estimate parameters, capturing true values through penalized terms. The algorithm simplifies complex nonlinear processes into various linear relationships in time-varying parameters with less computational efforts and concise structures. Simulation examples are provided to demonstrate the effectiveness of the algorithm and guide readers systematically.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Yanjiao Wang et al.
Summary: The Volterra model, although capable of representing a wide range of nonlinear dynamical systems, faces limitations in practical use for nonlinear system identification due to the exponential growth of Volterra kernel coefficients. This paper introduces a tensorial decomposition called PARAFAC to reduce the number of parameters in representing Volterra kernels compared to the traditional Volterra model. The proposed recursive algorithm, based on the multi-innovation identification theory and l(2)-norm, effectively handles PARAFAC-Volterra models with Gaussian noises, while the multi-innovation algorithm combined with logarithmic p-norms is studied for nonlinear Volterra systems with non-Gaussian noises. Simulation results demonstrate the effectiveness of the proposed identification methods.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Engineering, Mechanical
Rohit Rana et al.
Summary: The research focuses on accurately estimating parameters from compressed temporal data in the presence of noise. The proposed method utilizes the properties of recursive wavelet domain to selectively store noise-free data coefficients, achieving data compression. The algorithm can be implemented on any scalable VLSI circuit and has been experimentally demonstrated on the Omni Bundle robot.
NONLINEAR DYNAMICS
(2022)
Article
Automation & Control Systems
Sahar Yazdani et al.
Summary: This article studies the flocking problem of multiagent systems with a dynamic virtual leader for linear systems subject to time-varying uncertainties and external disturbance problems. By designing an adaptive controller and assigning estimators, this method ensures that the velocity of the agents converges to that of the virtual leader, while maintaining network connectivity and avoiding collisions.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Ling Xu
Summary: Signal modeling is an important technique in engineering applications, and this paper focuses on modeling sine multi-frequency signals or periodic signals using a separable modeling scheme. By utilizing a sliding measurement window, real-time signal information can be captured for accurate parameter estimation. The proposed method successfully models dynamic signals based on separable parameter sets.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Junwei Wang et al.
Summary: This article focuses on the parameter estimation issues for a fractional-order nonlinear system with autoregressive noise. A two-stage gradient-based iterative algorithm and a two-stage moving-data-window gradient-based iterative algorithm are proposed, and their effectiveness is verified through simulations.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Syed Ali Ajwad et al.
Summary: This article proposes a distributed formation tracking controller for multi-agent systems consisting of quadrotors. The control system is divided into an outer-loop and an inner-loop, using a continuous-discrete time observer to estimate the positions and velocities of the quadrotors and their neighbors. The position and attitude controllers are designed based on these estimated states to achieve the desired geometric shape and attitude.
NONLINEAR DYNAMICS
(2022)
Article
Engineering, Civil
Xi Wang et al.
Summary: This paper investigates the robust dynamic train regulation problem with respect to frequent disruptions and imperfect wireless transmissions in urban rail transit. A fuzzy passenger arrival rate and a T-S fuzzy state-space model are adopted to address the uncertainty of passenger flow, and a robust real-time train regulation strategy based on fuzzy predictive control theory is developed.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Jian Pan et al.
Summary: This paper proposes a hierarchical least squares algorithm for parameter identification problems of a Volterra nonlinear system. By decomposing the Volterra system into three subsystems with a smaller number of parameters and estimating the parameters of each subsystem separately, the proposed algorithm overcomes the excessive calculation amount of the Volterra systems. The calculation analysis shows that the proposed algorithm has lower computational cost compared to the recursive least squares algorithm, and simulation results demonstrate its effectiveness in identifying Volterra systems.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Multidisciplinary Sciences
M. Ausif Padder et al.
Summary: In this research paper, a four-dimensional tumor-macrophages response model is proposed to study the role of Treg cells in the growth and dynamics of the model. The qualitative properties, discrete counterpart, and stability analysis of the model are discussed. Numerical simulation is also performed to illustrate the theoretical results.
IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION A-SCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Muhammad Arif et al.
Summary: This study proposes a quantum calculus-based noisy links incremental least mean squares (NL-qILMS) algorithm, which introduces a q-parameter to search for local and global minima. Various time-varying techniques are also devised to select the optimal q-parameter for improved performance. The analytical results are validated through simulation.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Civil
Yuan Cao et al.
Summary: This study introduced a sound-based fault diagnosis method for railway point machines, achieving over 99% diagnosis accuracy through feature selection and ensemble classifier optimization.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Yongkui Sun et al.
Summary: The paper proposes a sound-based fault diagnosis method for railway point machines (RPMs) using fractional calculus and coarse-grain process. A novel feature called multi-scale fractional WPDE (FWPDE) is developed to improve the fault diagnosis accuracy. The paper also presents a synchronous optimization strategy based on binary particle swarm optimization (BPSO) to select optimal feature set and optimize the hyperparameters of support vector machine (SVM), further improving the diagnosis accuracy. The proposed method is verified to be superior and effective compared to existing fault diagnosis methods.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Automation & Control Systems
Meihang Li et al.
Summary: This paper studies iterative parameter estimation methods for a class of nonlinear systems with interval-varying measurements. By utilizing the auxiliary model identification idea, an auxiliary model is constructed to estimate the unknown noise-free process outputs, and an interval-varying auxiliary model gradient-based iterative identification algorithm is developed. Additionally, a particle filter is adopted to compute the output estimates using discrete random sampling points to approximate the posterior probability density function. Furthermore, an interval-varying particle filtering gradient-based iterative algorithm is derived, and a V-AM-SG algorithm based on the interval-varying auxiliary model is presented for comparison. Simulation results demonstrate the effectiveness of the proposed algorithms in identifying nonlinear systems with interval-varying measurements and their ability to generate more accurate parameter estimates compared to the V-AM-SG algorithm.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Automation & Control Systems
Yanjiao Wang et al.
Summary: This article presents a method for modeling NARX systems using tensor network B-spline (TNBS), which reduces the computational and storage burden for high-dimensional systems through the representation of multivariate B-spline weight tensors. The recursive algorithm proposed for NARX systems with Gaussian noise combines the multi-innovation identification theory and the hierarchical identification principle, using the l2-norm. TNBS can fit nonlinear systems with strong nonlinearity by adjusting the degree and number of knots. A numerical experiment is conducted to demonstrate the effectiveness of the algorithm.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Engineering, Electrical & Electronic
Cao Yuan et al.
Summary: This study investigates the train operation control method based on virtual coupling in order to improve transportation efficiency in rail transit systems. A virtual coupled train dynamics model with nonlinear dynamics is established, and the parameters of the model are identified using real-time data. A fusion controller is used for train control and collision prevention, which is shown to be effective through the use of real high-speed railway data.
CHINESE JOURNAL OF ELECTRONICS
(2022)
Article
Automation & Control Systems
Jiaxin Xiong et al.
Summary: In this article, a sliding mode dual-channel disturbance rejection control method is proposed for the attitude control of a quadrotor under unknown disturbances. The proposed method compensates for the low-frequency and high-frequency components of the disturbances and reduces the influence of the virtual disturbance estimation error. The stability of the system is proved using Lyapunov theory.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Automation & Control Systems
Xuena Zhao et al.
Summary: This article considers the problem of time-varying trajectory tracking boundary control for a flexible rotation beam subject to distributed disturbance. A partial differential equation model is established for the flexible rotation beam system with the coupling of angle and vibration displacement. A boundary controller is proposed to track the desired time-varying trajectory by introducing a servo system generated by the reference signal. Lyapunov-based stability analysis and infinite dimensional operator theory are used to prove the boundedness of the tracking error system and the closed-loop system. Numerical simulations and experiments on the Quanser platform demonstrate the correctness of the theory and the effectiveness of the control strategy.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Civil
Yuan Cao et al.
Summary: This paper proposes a trajectory optimization approach for high-speed trains aiming to reduce traction energy consumption and increase riding comfort. The approach can also achieve energy-saving effects by optimizing the operation time between stations. The optimization model considers factors such as discrete throttle settings, neutral zones, and sectionalized tunnel resistance. The model is then discretized and turned into a multi-step decision optimization problem. Simulation results with real-world data demonstrate the effectiveness of the proposed approach in saving energy and improving riding comfort.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Jimei Li et al.
Summary: This paper utilizes the fitting advantages of Gaussian and polynomial functions to propose a more widely applicable nonlinear signal model, and investigates the parameter estimation issues in the presence of noise. By using the stability factor recursive algorithm and the hierarchical identification principle, a computationally efficient recursive algorithm for nonlinear signals is developed, and the effectiveness of the proposed algorithms is tested through simulation experiments.
IEEE SIGNAL PROCESSING LETTERS
(2022)
Article
Automation & Control Systems
Meihang Li et al.
Summary: A dual-rate identification model and two iterative algorithms were proposed for identifying parameters of dual-rate sampled-data stochastic systems, with a hierarchical identification approach used to improve computation efficiency. Simulation results showed the effectiveness of the algorithms, with the H-ML-LSI algorithm demonstrating higher computational efficiency compared to the ML-LSI algorithm.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2021)
Article
Automation & Control Systems
Yan Ji et al.
Summary: This article focuses on parameter estimation for a class of nonlinear systems, introducing a forgetting factor stochastic gradient estimation method to improve accuracy by decomposing the system into three subsystems. A three-stage forgetting factor stochastic gradient algorithm is proposed based on the hierarchical identification principle. Simulation results show the effectiveness of the algorithm.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Jie Hou et al.
Summary: The article introduces a gray-box parsimonious subspace identification method for block-oriented Hammerstein-type systems, utilizing both dynamic and steady-state data to improve accuracy, especially for highly nonlinear models. This method reduces parameter estimation errors and enhances model accuracy by utilizing parsimonious models and hierarchical estimation techniques.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Engineering, Electrical & Electronic
Yongkui Sun et al.
Summary: The novel method combines EMD denoising and MFPE multi-scale fractional permutation entropy, avoiding information loss through a two-scale algorithm. Experimental results demonstrate the method's significant advantages in railway point machine fault diagnosis.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Automation & Control Systems
Ling Xu et al.
Summary: This paper investigates the parameter estimation for multifrequency sine signals by separating the characteristic parameters into linear and nonlinear sets and constructing corresponding identification submodels for optimization. By introducing a forgetting factor to track time-varying information, the proposed method is validated through numerical examples based on performance measures.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2021)
Article
Automation & Control Systems
Junwei Wang et al.
Summary: This article discusses the parameter estimation for a fractional-order nonlinear finite impulse response system with colored noise. It proposes a solution to reduce the problem of redundant parameter estimation by expressing the output form of the system as a linear combination of unknown parameters through key term separation, and achieves higher estimation accuracy using gradient-based iterative algorithms and multiinnovation theory. The simulation results confirm the effectiveness of the proposed algorithms.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2021)
Article
Automation & Control Systems
Ximei Liu et al.
Summary: This article explores the parameter estimation issue of input nonlinear controlled systems with variable-gain nonlinearity and proposes different algorithms for parameter estimation. The results indicate that the maximum likelihood extended gradient-based iterative algorithm has better estimation accuracy and fitting performance.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Feng Ding et al.
Summary: This paper proposes new algorithms for parameter identification problems of IN-OE systems, which effectively reduce the computation amount and improve efficiency through the hierarchical identification principle and the key term separation auxiliary model approach.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Engineering, Mechanical
Hossein Mohamadipanah et al.
Summary: A novel kernel-based algorithm for modeling evenly distributed multidimensional datasets is introduced in this study, which does not rely on input space sparsification. The proposed method reorganizes the typical single-layer kernel-based model into a deep hierarchical structure, resulting in significant computational speedup and improved modeling accuracy.
NONLINEAR DYNAMICS
(2021)
Article
Engineering, Mechanical
Stefano Fazzino et al.
Summary: This work explores the application of fractional-order Hopfield neural networks in solving optimization problems and online parameter estimation for nonlinear dynamical systems. The study shows how fractional-order neurons impact the convergence of the Hopfield network, improving parameter identification performance compared to integer-order implementations. By considering and comparing different methods for computing fractional derivatives, the Caputo and Caputo-Fabrizio definitions were evaluated based on simulation results with various benchmarks, demonstrating the effectiveness of the proposed architecture for online parameter estimation.
NONLINEAR DYNAMICS
(2021)
Article
Engineering, Multidisciplinary
Yuan Cao et al.
Summary: This paper proposes a method for dynamic marshalling of trains based on virtual coupling to reduce passenger density at stations, addressing the challenges faced by urban rail transit operations during the epidemic. The study analyzes the infection risk of passenger subway travel under virtual coupling and compares it with traditional train control systems, finding that the risk of infection is lower under the virtual coupling system. Effective measures can be implemented in conjunction with virtual coupling to further reduce infection risk.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Automation & Control Systems
David Scott et al.
Summary: Multivariate statistical process monitoring methods provide sensitive indicators of process conditions by utilizing large amounts of process data. A novel nonstationary probabilistic slow feature analysis algorithm is developed to comprehensively describe nonstationary and stationary variations, with the expectation-maximization algorithm used for efficient parameter estimation. Interpretable monitoring statistics are constructed to detect abnormalities in nonstationary and stationary dynamics, forming a holistic and pragmatic monitoring framework for industrial processes.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2021)
Article
Automation & Control Systems
Jiling Ding et al.
Summary: This article proposes a new adaptive finite-time tracking control method for nonlinear systems with uncertain parameters, utilizing command filters and compensation signals to solve the trajectory tracking problem. The proposed control method ensures the tracking error remains in a small neighborhood of the origin in finite time.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2021)
Article
Automation & Control Systems
Zhen Kang et al.
Summary: This article proposes a hierarchical recursive least squares algorithm to estimate the parameters of Hammerstein nonlinear autoregressive output-error systems. By decomposing the original system into three subsystems, the algorithm successfully identifies the parameters of each subsystem interactively. Simulation results confirm the effectiveness of the proposed algorithm in parameter estimation.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2021)
Article
Automation & Control Systems
Meihang Li et al.
Summary: This article introduces the AM-LSI and PF-LSI algorithms for iterative parameter estimation of nonlinear systems, showing that they are effective in improving parameter estimation accuracy compared to traditional algorithms.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2021)
Article
Automation & Control Systems
Ping Ma et al.
Summary: This article proposes a solution to the parameter estimation issues for a class of multivariate control systems with colored noise, deriving two different least squares algorithms and confirming their effectiveness through numerical examples.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2021)
Article
Automation & Control Systems
Yan Ji et al.
Summary: This paper discusses the parameter estimation problem of a two-input single-output Hammerstein finite impulse response system with autoregressive moving average noise. A filtering based multi-innovation stochastic gradient algorithm is proposed for this system, demonstrating effective parameter estimation results.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2021)
Article
Energy & Fuels
Meng Jiao et al.
Summary: This paper presents a SG-BiLSTM based method for SOC estimation of lithium batteries, which demonstrates advantages such as faster convergence speed, higher estimation accuracy, and strong robustness through experimental and simulation verification.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Automation & Control Systems
Yanjiao Wang et al.
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