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Data Generation Feedback Relearning Control for Unmodeled Nonlinear Systems

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第1期8卷 389-400页

作者： Zhang, Yong Mu, Chaoxu Zhao, Dongbin Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

Reinforcement learning (RL) algorithms require continuous interaction with the controlled plant to optimize the objective function and control policy. However, the security risk and poor real-time performance limit the application of RL algorithms. In this article, a data generation feedback relearning (DGFR) control algorithm is developed to avoid these limitations. The DGFR control algorithm improves the control performance by interacting with a proposed delayed neural network-based data generation model. Considering the time-delay characteristics of industrial production, the data generation model is different from the identifier of the system model, but it is used to generate the target data required by the DGFR control algorithm. The interaction between the algorithm and the data generation model avoids the risk of trial-and-error and improves real-time performance. Finally, the superiority of the proposed control algorithm is verified by giving comparative experiments in conjunction with a power grid system and a permanent magnet synchronous motor system.

关键词： Data models Real-time systems Heuristic algorithms Mathematical models Adaptation models approximation algorithms Cost function Data generation model feedback relearning control delayed neural network reinforcement learning unmodeled nonlinear system

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Robust Transmission Design for RIS-Assisted Integrated Sensing and Communication Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第11期73卷 17151-17164页

作者： Xu, Yongqing Li, Yong Quek, Tony Q. S. Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Beijing 100876 Peoples R China Singapore Univ Technol & Design Singapore 487372 Singapore

As a critical technology for next-generation communication networks, integrated sensing and communication (ISAC) aims to achieve the harmonious coexistence of communication and sensing. The degrees-of-freedom (DoF) of ISAC is limited due to multiple performance metrics used for communication and sensing. Reconfigurable Intelligent Surfaces (RIS) composed of metamaterials can enhance the DoF in the spatial domain of ISAC systems. However, the availability of perfect Channel State Information (CSI) is a prerequisite for the gain brought by RIS, which is not realistic in practical environments. Therefore, under the imperfect CSI condition, we propose a decomposition-based large deviation inequality approach to eliminate the impact of CSI error on communication rate and sensing Cram & eacute;r-Rao bound (CRB). Then, an alternating optimization (AO) algorithm based on semi-definite relaxation (SDR) and gradient extrapolated majorization-maximization (GEMM) is proposed to solve the transmit beamforming and discrete RIS beamforming problems. We also analyze the complexity and convergence of the proposed algorithm. Simulation results show that the proposed algorithms can effectively eliminate the influence of CSI error and have good convergence performance. Notably, when CSI error exists, the gain brought by RIS will decrease with the increase of the number of RIS elements.

关键词： Array signal processing Integrated sensing and communication Radar Receiving antennas Interference approximation algorithms Vectors joint communication and sensing reconfigurable intelligent surfaces intelligent reflecting surfaces robust beamforming design

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Policy Iterative-Based Adaptive Optimal Control for Unknown Continuous-Time Nonlinear Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第4期55卷 2859-2869页

作者： Fang, Haiyang He, Shuping Liu, Fei Ding, Zhengtao Anhui Univ Sch Elect Engn & Automat Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230026 Peoples R China Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL England

This study addresses the optimal control problem for continuous-time nonlinear systems with unknown dynamics. A policy iterative-based optimization algorithm is proposed to solve this problem by leveraging a novel neural network representation termed multivariable neural network linear differential inclusion (MVNNLDI). MVNNLDI approximates the initial nonlinear model with a linear differential equation formulation that includes bounded disturbances. Based on this linearized representation, the relevant adaptive optimal control and disturbance compensation approach are derived to tackle the nonlinear optimization problem. Capitalizing on model-free control principles, the optimal solutions can be obtained using only measured state and input data, thus simplifying algorithmic complexity and accelerating convergence speed substantially. Finally, we use two simulation experiments to demonstrate the feasibility and effectiveness of the proposed method.

关键词： Optimal control Mathematical models Artificial neural networks Heuristic algorithms Adaptation models Adaptive systems Neurons Stability analysis Nonlinear dynamical systems approximation algorithms Adaptive optimal control continuous-time nonlinear systems multivariable neural network linear differential inclusion (MVNNLDI) policy iteration (PI)

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Generalization Performance of Empirical Risk Minimization on Over-Parameterized Deep ReLU Nets

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第3期71卷 1978-1993页

作者： Lin, Shao-Bo Wang, Yao Zhou, Ding-Xuan Xi An Jiao Tong Univ Ctr Intelligent Decis Making & Machine Learning Sch Management Xian 710049 Peoples R China Univ Sydney Sch Math & Stat Sydney NSW 2006 Australia

In this paper, we study the generalization performance of global minima of empirical risk minimization (ERM) on over-parameterized deep ReLU nets. Using a novel deepening scheme for deep ReLU nets, we rigorously prove that there exist perfect global minima achieving optimal generalization error rates for numerous types of data under mild conditions. Since over-parameterization of deep ReLU nets is crucial to guarantee that the global minima of ERM can be realized by the widely used stochastic gradient descent (SGD) algorithm, our results present a potential way to fill the gap between optimization and generalization of deep learning.

关键词： Deep learning Optimization Convergence approximation algorithms Error analysis Machine learning algorithms Analytical models Sparse matrices Kernel approximation error empirical risk minimization global minima over-parameterization

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Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 4286-4301页

作者： Lin, Zhidi Sun, Yiyong Yin, Feng Thiery, Alexandre Hoang Chinese Univ Hong Kong Sch Sci & Engn Future Network Intelligence Inst Shenzhen 518172 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Natl Univ Singapore Dept Stat & Data Sci Singapore 117546 Singapore Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China

The Gaussian process state-space models (GPSSMs) represent a versatile class of data-driven nonlinear dynamical system models. However, the presence of numerous latent variables in GPSSM incurs unresolved issues for existing variational inference approaches, particularly under the more realistic non-mean-field (NMF) assumption, including extensive training effort, compromised inference accuracy, and infeasibility for online applications, among others. In this paper, we tackle these challenges by incorporating the ensemble Kalman filter (EnKF), a well-established model-based filtering technique, into the NMF variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO). Moreover, owing to the streamlined parameterization via the EnKF, the new GPSSM model can be easily accommodated in online learning applications. We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting. We also provide detailed analysis and fresh insights for the proposed algorithms. Comprehensive evaluation across diverse real and synthetic datasets corroborates the superior learning and inference performance of our EnKF-aided variational inference algorithms compared to existing methods.

关键词： Heuristic algorithms Signal processing algorithms Inference algorithms Gaussian processes Computational modeling Kalman filters approximation algorithms Gaussian process state-space model ensemble Kalman filter online learning variational inference

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Probabilistic Contraction Analysis of Iterated Random Operators

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第9期69卷 5947-5962页

作者： Gupta, Abhishek Jain, Rahul Glynn, Peter Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Univ Southern Calif Dept Elect Engn Dept Comp Sci Los Angeles CA 90089 USA Univ Southern Calif USC Ctr Auton & AI Los Angeles CA 90089 USA Stanford Univ Inst Computat & Math Engn Dept Management Sci & Engn Stanford CA 94305 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA

In many branches of engineering, Banach contraction mapping theorem is employed to establish the convergence of certain deterministic algorithms. Randomized versions of these algorithms have been developed that have proved useful in data-driven problems. In a class of randomized algorithms, in each iteration, the contraction map is approximated with an operator that uses independent and identically distributed samples of certain random variables. This leads to iterated random operators acting on an initial point in a complete metric space, and it generates a Markov chain. In this article, we develop a new stochastic dominance-based proof technique, called probabilistic contraction analysis, for establishing the convergence in probability of Markov chains generated by such iterated random operators in certain limiting regime. The methods developed in this article provides a general framework for understanding convergence of a wide variety of Monte Carlo methods, in which contractive property is present. We apply the convergence result to conclude the convergence of fitted value iteration and fitted relative value iteration in continuous state and continuous action MDPs as representative applications of the general framework developed here.

关键词： Probabilistic logic Markov processes approximation algorithms Convergence Costs Extraterrestrial measurements Monte Carlo methods Convergence of numerical methods iterative algorithms optimization

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Oversampled Low Ambiguity Zone Sequences for Channel Estimation Over Doubly Selective Channels

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第4期73卷 2616-2630页

作者： Gu, Zhi Zhou, Zhengchun Fan, Pingzhi Adhikary, Avik Ranjan Liu, Zilong Southwest Jiaotong Univ Chengdu 611756 Peoples R China Southwest Jiaotong Univ Inst Mobile Commun Chengdu 611756 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England

Pilot sequence design over doubly selective channels (DSC) is challenging due to the variations in both the time- and frequency-domains. Against this background, the contribution of this paper is twofold: Firstly, we investigate the optimal sequence design criteria for efficient channel estimation in orthogonal frequency division multiplexing systems under DSC. Secondly, to design pilot sequences that can satisfy the derived criteria, we propose a new metric called oversampled ambiguity function (O-AF), which considers both fractional and integer Doppler frequency shifts. Optimizing the sidelobes of O-AF through a modified iterative twisted approximation (ITROX) algorithm, we develop a new class of pilot sequences called "oversampled low ambiguity zone (O-LAZ) sequences". Through numerical experiments, we evaluate the efficiency of the proposed O-LAZ sequences over the traditional low ambiguity zone (LAZ) sequences, Zadoff-Chu (ZC) sequences and m-sequences, by comparing their channel estimation performances over DSC.

关键词： Channel estimation OFDM Correlation Complexity theory Optimization Iterative algorithms Doppler shift approximation algorithms Heuristic algorithms Frequency estimation Ambiguity function channel estimation doubly selective channel ITROX algorithm orthogonal frequency division multiplexing (OFDM) oversampled ambiguity zone (O-AZ) sequences

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Quality Indicators for Preference-Based Evolutionary Multiobjective Optimization Using a Reference Point: A Review and Analysis

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2024年第6期28卷 1575-1589页

作者： Tanabe, Ryoji Li, Ke Yokohama Natl Univ Fac Environm & Informat Sci Yokohama 2408501 Japan Univ Exeter Dept Comp Sci Exeter EX4 4QF England

Some quality indicators have been proposed for benchmarking preference-based evolutionary multiobjective optimization (EMO) algorithms using a reference point. Although a systematic review and analysis of the quality indicators are helpful for both benchmarking and practical decision making, neither has been conducted. In this context, first, this article reviews existing regions of interest and quality indicators for preference-based EMO using the reference point. We point out that each quality indicator was designed for a different region of interest. Then, this article investigates the properties of the quality indicators. We demonstrate that an achievement scalarizing function value is not always consistent with the distance from a solution to the reference point in the objective space. We observe that the regions of interest can be significantly different depending on the position of the reference point and the shape of the Pareto front. We identify undesirable properties of some quality indicators. We also show that the ranking of preference-based EMO algorithms depends on the choice of quality indicators.

关键词： Optimization Behavioral sciences approximation algorithms Linear programming Benchmark testing Surveys Quality assessment Benchmarking preference-based evolutionary multiobjective optimization (EMO) quality indicators

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An Efficient Hybrid Domain Algorithm for Accurate SAR Raw Data Generation With Trajectory Deviations

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Wan, Bo Zhang, Lei Wu, Jianxin Wang, Guanyong Xi, Zirui Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China North China Univ Technol Sch Informat & Sci Beijing 100144 Peoples R China

An efficient raw data generation (RDG) algorithm in the hybrid domain is proposed, which can be applied to the accurate echo generation of spotlight mode synthetic aperture radar (SAR) with trajectory deviation, even in cases of terrain undulation. Generally, the accuracy required for the signal's envelope is on the order of the range resolution cell. However, the requirement for the phase is significantly higher, on the order of the wavelength. Therefore, the proposed algorithm calculates the phase of the SAR raw data in the time domain through a point-by-point approach to ensure accuracy and computes the envelope of the raw data through subblock processing in the frequency domain to improve efficiency. To balance the computational efficiency and accuracy, the optimal selection of subblock size is discussed in detail. Simulation experiments verify the accuracy and efficiency of the method.

关键词： Time-domain analysis approximation algorithms Accuracy Frequency-domain analysis Synthetic aperture radar Azimuth Trajectory Doppler effect Imaging Data collection Raw data generation (RDG) subblock processing synthetic aperture radar (SAR) trajectory deviation

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Recursive Forward-Backward EDMD: Guaranteed Algebraic Search for Koopman Invariant Subspaces

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IEEE ACCESS 2025年 13卷 61006-61025页

作者： Haseli, Masih Cortes, Jorge Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA

The implementation of the Koopman operator on digital computers often relies on the approximation of its action on finite-dimensional function spaces. This approximation is generally done by orthogonally projecting on the subspace. Extended Dynamic Mode Decomposition (EDMD) is a popular, special case of this projection procedure in a data-driven setting. Importantly, the accuracy of the model obtained by EDMD depends on the quality of the finite-dimensional space, specifically on how close it is to being invariant under the Koopman operator. This paper presents a data-driven algebraic search algorithm, termed Recursive Forward-Backward EDMD, for subspaces close to being invariant under the Koopman operator. Relying on the concept of temporal consistency, which measures the quality of the subspace, our algorithm recursively decomposes the search space into two subspaces with different prediction accuracy levels. The subspace with lower level of accuracy is removed if it does not reach a satisfactory threshold. The algorithm allows for tuning the level of accuracy depending on the underlying application and is endowed with convergence and accuracy guarantees.

关键词： Accuracy Eigenvalues and eigenfunctions Vectors Prediction algorithms Heuristic algorithms Hands Pollution measurement Complex systems approximation algorithms Transmission line matrix methods Accuracy bound algebraic algorithm dynamic mode decomposition invariant subspace Koopman operator unknown nonlinear system

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