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Author details

Xin Luo

Publications

209

Citations

9,369

Publications by Year

20122025

Co-Authors:

Yusuf Al-TurkiAhmed AlabdulwahabAiiad AlbeshriAreej AlhothaliAhmed Chiheb Ammari

Show All Co-Authors (225)

Xin Luo

Affiliation

College of Computer and Information Science

Southwest University

Chongqing, China

Publication Topics

Root Mean Square Error,
Computational Efficiency,
Representation Learning,
Time Cost,
Learning Strategies,
Convergence Rate,
Neural Network,
Objective Function,
Stochastic Gradient Descent,
Non-negative Matrix Factorization,
Recommender Systems,
Latent Factor Analysis

Biography

Xin Luo (Senior Member, IEEE) received the Ph.D. degree in computer science from Beihang University, Beijing, China, in 2011. In 2016, he joined the Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China, as a Professor of computer science and engineering. He is currently a Distinguished Professor of computer science with the Dongguan University of Technology, Dongguan, China. His current research interests include big data analysis and intelligent control. He has published over 160 articles (including over 60 IEEE transactions articles) in the above areas.(Based on document published on 7 July 2022).

Publications

209

Citations

9,369

Publications by Year

20122025

Co-Authors:

Yusuf Al-Turki
Ahmed Alabdulwahab
Aiiad Albeshri
Areej Alhothali
Ahmed Chiheb Ammari

Show All Co-Authors (225)

Author's Published Works

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Showing 1-25 of 209 results

Journals (149)

Conferences (58)

Early Access Articles (2)

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Xin Luo(209)
MengChu Zhou(53)
Shuai Li(45)
Long Jin(24)
Mingsheng Shang(22)
College of Computer and Information Science, Southwest University, Chongqing, China(70)
Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA(43)
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China(42)
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China(35)
Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China(30)
IEEE/CAA Journal of Automatica Sinica(18)
IEEE Transactions on Neural Networks and Learning Systems(17)
IEEE Transactions on Industrial Informatics(15)
IEEE Transactions on Systems, Man, and Cybernetics: Systems(13)
IEEE Transactions on Cybernetics(10)
IEEE(209)
Media(32)
Melbourne, Australia(6)
Honolulu, Oahu, HI, USA(5)
Chicago, IL, USA(4)
Lyon, France(4)
Zhuhai, China(4)
Root Mean Square Error(112)
Objective Function(62)
Computational Efficiency(61)
Recommender Systems(58)
Latent Factors(56)

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Results

Learning Error Refinement in Stochastic Gradient Descent-Based Latent Factor Analysis via Diversified PID Controllers

Jinli Li;Ye Yuan;Xin Luo

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2025 | Early Access Article |

In Big Data-based applications, high-dimensional and incomplete (HDI) data are frequently used to represent the complicated interactions among numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model can process such data efficiently. Unfortunately, a standard SGD algorithm trains a single latent factor relying on the stochastic gradient related to the current l...Show More

Learning Error Refinement in Stochastic Gradient Descent-Based Latent Factor Analysis via Diversified PID Controllers

Jinli Li;Ye Yuan;Xin Luo

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2025 | Early Access Article |

An Adaptive p-Norms-Based Kinematic Calibration Model for Industrial Robot Positioning Accuracy Promotion

Tinghui Chen;Weiyi Yang;Shuai Li;Xin Luo

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2025 | Volume: 55, Issue: 4 | Journal Article |

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Industrial robots inevitably incur kinematic errors in the advanced manufacturing and assembly processes, resulting in the severe reduction of the absolute positioning accuracy (APA). Kinematic calibration (KC) is well-known as a vital technique in APA-promoting tasks. However, existing KC models generally adopt a single distance-oriented Loss, e.g., an

$L_{2}$ norm-oriented one that neglects th...Show More

An Adaptive p-Norms-Based Kinematic Calibration Model for Industrial Robot Positioning Accuracy Promotion

Tinghui Chen;Weiyi Yang;Shuai Li;Xin Luo

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2025 | Volume: 55, Issue: 4 | Journal Article |

Fourth-Order Dimension Preserved Tensor Completion With Temporal Constraint for Missing Traffic Data Imputation

Hong Chen;Mingwei Lin;Liang Zhao;Zeshui Xu;Xin Luo

IEEE Transactions on Intelligent Transportation Systems

Year: 2025 | Early Access Article |

In the intelligent transportation system, the collected traffic data are usually incomplete. The low-rank completion models are effective in processing missing traffic data (MTD) imputation. However, the existing low-rank completion models encounter the following challenges: 1) Incorporating spatio-temporal information of traffic data often leads to significant time overhead; 2) To reduce the time...Show More

Fourth-Order Dimension Preserved Tensor Completion With Temporal Constraint for Missing Traffic Data Imputation

Hong Chen;Mingwei Lin;Liang Zhao;Zeshui Xu;Xin Luo

IEEE Transactions on Intelligent Transportation Systems

Year: 2025 | Early Access Article |

Graph Linear Convolution Pooling for Learning in Incomplete High-Dimensional Data

Fanghui Bi;Tiantian He;Yew-Soon Ong;Xin Luo

IEEE Transactions on Knowledge and Data Engineering

Year: 2025 | Volume: 37, Issue: 4 | Journal Article |

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High-dimensional and incomplete (HDI) data are frequently encountered in diverse real-world applications involving complex interactions among numerous nodes. Approaches based on latent feature analysis (LFA) have proven effective in performing representation learning in HDI data. Nevertheless, they cannot handle the high-order connectivity among nodes in HDI data well, resulting in severe accuracy...Show More

Graph Linear Convolution Pooling for Learning in Incomplete High-Dimensional Data

Fanghui Bi;Tiantian He;Yew-Soon Ong;Xin Luo

IEEE Transactions on Knowledge and Data Engineering

Year: 2025 | Volume: 37, Issue: 4 | Journal Article |

Latent Factor Analysis Model With Temporal Regularized Constraint for Road Traffic Data Imputation

Hengshuo Yang;Mingwei Lin;Hong Chen;Xin Luo;Zeshui Xu

IEEE Transactions on Intelligent Transportation Systems

Year: 2025 | Volume: 26, Issue: 1 | Journal Article |

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Intelligent Transportation Systems (ITSs) are designed to alleviate traffic congestion and provide convenience for travelers or decision-makers. However, the challenge lies in obtaining complete and accurate traffic data because of various factors. Therefore, many models have been studied for traffic data imputation. Most studies overlook the extreme case where the missing rate of observed traffic...Show More

Latent Factor Analysis Model With Temporal Regularized Constraint for Road Traffic Data Imputation

Hengshuo Yang;Mingwei Lin;Hong Chen;Xin Luo;Zeshui Xu

IEEE Transactions on Intelligent Transportation Systems

Year: 2025 | Volume: 26, Issue: 1 | Journal Article |

A 3D Convolution-Incorporated Dimension Preserved Decomposition Model for Traffic Data Prediction

Mingwei Lin;Jiaqi Liu;Hong Chen;Xiuqin Xu;Xin Luo;Zeshui Xu

IEEE Transactions on Intelligent Transportation Systems

Year: 2025 | Volume: 26, Issue: 1 | Journal Article |

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Traffic data prediction is a crucial component of Intelligent Transport Systems (ITS) as it contributes significantly to real-time navigation and congestion management. However, due to incomplete deployment of sensor and instability of data transmission, the traffic data that we collect is usually a high-dimensional and incomplete (HDI) matrix or tensor. Currently, there are two challenges with tr...Show More

A 3D Convolution-Incorporated Dimension Preserved Decomposition Model for Traffic Data Prediction

Mingwei Lin;Jiaqi Liu;Hong Chen;Xiuqin Xu;Xin Luo;Zeshui Xu

IEEE Transactions on Intelligent Transportation Systems

Year: 2025 | Volume: 26, Issue: 1 | Journal Article |

An Outlier-Resilient Autoencoder for Representing High-Dimensional and Incomplete Data

Di Wu;Yuanpeng Hu;Kechen Liu;Jing Li;Xianmin Wang;Song Deng;Nenggan Zheng;Xin Luo

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2025 | Volume: 9, Issue: 2 | Journal Article |

Cited by: Papers (1)

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High-dimensional and incomplete (HDI) data commonly arise in various Big Data-related applications, e.g., recommender systems and bioinformatics. Representation is a learning paradigm to map HDI data into low-dimensional latent space for attracting valuable knowledge and patterns. Currently, deep neural network (DNN) is one of the most popular and successful approaches to represent HDI data due to...Show More

An Outlier-Resilient Autoencoder for Representing High-Dimensional and Incomplete Data

Di Wu;Yuanpeng Hu;Kechen Liu;Jing Li;Xianmin Wang;Song Deng;Nenggan Zheng;Xin Luo

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2025 | Volume: 9, Issue: 2 | Journal Article |

A Calibrator Fuzzy Ensemble for Highly-Accurate Robot Arm Calibration

Xin Luo;Zhibin Li;Wenbin Yue;Shuai Li

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Volume: 36, Issue: 2 | Journal Article |

Cited by: Papers (6)

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The absolute positioning accuracy of an industrial robot arm is vital for advancing manufacturing-related applications like automatic assembly, which can be improved via the data-driven approaches to robot arm calibration. Existing data-driven calibrators have illustrated their efficiency in addressing the issue of robot arm calibration. However, they mostly are single learning models that can be ...Show More

A Calibrator Fuzzy Ensemble for Highly-Accurate Robot Arm Calibration

Xin Luo;Zhibin Li;Wenbin Yue;Shuai Li

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Volume: 36, Issue: 2 | Journal Article |

Robust Low-Rank Latent Feature Analysis for Spatiotemporal Signal Recovery

Di Wu;Zechao Li;Zhikai Yu;Yi He;Xin Luo

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Volume: 36, Issue: 2 | Journal Article |

Cited by: Papers (20)

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Wireless sensor network (WSN) is an emerging and promising developing area in the intelligent sensing field. Due to various factors like sudden sensors breakdown or saving energy by deliberately shutting down partial nodes, there are always massive missing entries in the collected sensing data from WSNs. Low-rank matrix approximation (LRMA) is a typical and effective approach for pattern analysis ...Show More

Robust Low-Rank Latent Feature Analysis for Spatiotemporal Signal Recovery

Di Wu;Zechao Li;Zhikai Yu;Yi He;Xin Luo

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Volume: 36, Issue: 2 | Journal Article |

A Generalized Nesterov-Accelerated Second-Order Latent Factor Model for High-Dimensional and Incomplete Data

Weiling Li;Renfang Wang;Xin Luo

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Volume: 36, Issue: 1 | Journal Article |

Cited by: Papers (11)

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High-dimensional and incomplete (HDI) data are frequently encountered in big date-related applications for describing restricted observed interactions among large node sets. How to perform accurate and efficient representation learning on such HDI data is a hot yet thorny issue. A latent factor (LF) model has proven to be efficient in addressing it. However, the objective function of an LF model i...Show More

A Generalized Nesterov-Accelerated Second-Order Latent Factor Model for High-Dimensional and Incomplete Data

Weiling Li;Renfang Wang;Xin Luo

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Volume: 36, Issue: 1 | Journal Article |

Vibration Control using A Robust Input Shaper via Extended Kalman Filter-Incorporated Residual Neural Network

Weiyi Yang;Shuai Li;Xin Luo

2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2024 | Conference Paper |

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With the rapid development of industry, there is a growing concern over the vibration control challenges associated with flexible structures and underactuated systems. Input shaping technology enables stable performance for high-speed motion in industrial motion systems. However, existing input shapers commonly suffer from the ineffective control performance caused by the ignorance of observation ...Show More

Vibration Control using A Robust Input Shaper via Extended Kalman Filter-Incorporated Residual Neural Network

Weiyi Yang;Shuai Li;Xin Luo

2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2024 | Conference Paper |

A Fine-Grained Regularization Scheme for Non-negative Latent Factorization of High-Dimensional and Incomplete Tensors

Hao Wu;Yan Qiao;Xin Luo

IEEE Transactions on Services Computing

Year: 2024 | Volume: 17, Issue: 6 | Journal Article |

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A Dynamically Weighted Directed Network (DWDN) fundamentally illustrates the complex interactions among massive nodes from a big-data-oriented application, like the dynamic interactions among numerous terminals in a metropolitan network management system (MNMS). A High-Dimensional and Incomplete (HDI) tensor is able to flexibly quantize it, where lots of entries are missing primarily due to the im...Show More

A Fine-Grained Regularization Scheme for Non-negative Latent Factorization of High-Dimensional and Incomplete Tensors

Hao Wu;Yan Qiao;Xin Luo

IEEE Transactions on Services Computing

Year: 2024 | Volume: 17, Issue: 6 | Journal Article |

A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

Jiufang Chen;Kechen Liu;Xin Luo;Ye Yuan;Khaled Sedraoui;Yusuf Al-Turki;MengChu Zhou

IEEE/CAA Journal of Automatica Sinica

Year: 2024 | Volume: 11, Issue: 11 | Journal Article |

Cited by: Papers (1)

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High-dimensional and incomplete (HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis (LFA) model is capable of conducting efficient representation learning to an HDI matrix, whose hyper-parameter adaptation can be implemented through a particle swarm optimizer (PSO) to meet scalable requirements. However, conventional PSO is limit...Show More

A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

Jiufang Chen;Kechen Liu;Xin Luo;Ye Yuan;Khaled Sedraoui;Yusuf Al-Turki;MengChu Zhou

IEEE/CAA Journal of Automatica Sinica

Year: 2024 | Volume: 11, Issue: 11 | Journal Article |

An Efficient Industrial Robot Calibrator With Multiplaner Constraints

Tinghui Chen;Weiyi Yang;Zhetao Zhang;Xin Luo

IEEE Transactions on Industrial Informatics

Year: 2024 | Volume: 20, Issue: 12 | Journal Article |

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Calibration technology has become an essential part of the task of improving robot precision. However, existing calibration schemes suffer from accuracy loss in actual working conditions due to the ignorance of the target robot's constrained workspace. In response to this challenge, this study proposes a novel robot calibrator with three-fold ideas: 1) optimizing the measurement configuration via ...Show More

An Efficient Industrial Robot Calibrator With Multiplaner Constraints

Tinghui Chen;Weiyi Yang;Zhetao Zhang;Xin Luo

IEEE Transactions on Industrial Informatics

Year: 2024 | Volume: 20, Issue: 12 | Journal Article |

Data Driven Vibration Control: A Review

Weiyi Yang;Shuai Li;Xin Luo

IEEE/CAA Journal of Automatica Sinica

Year: 2024 | Volume: 11, Issue: 9 | Journal Article |

Cited by: Papers (3)

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With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing, aviation, and healthcare, the data driven vibration control (DDVC) has attracted broad interests from both the industrial and academic communities. Input shaping (IS), as a simple and effective feedforward method, is greatly demanded in DDVC methods. It convolves the desire...Show More

Data Driven Vibration Control: A Review

Weiyi Yang;Shuai Li;Xin Luo

IEEE/CAA Journal of Automatica Sinica

Year: 2024 | Volume: 11, Issue: 9 | Journal Article |

Alternating-Direction-Method of Multipliers-Based Adaptive Nonnegative Latent Factor Analysis

Yurong Zhong;Kechen Liu;Shangce Gao;Xin Luo

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 5 | Journal Article |

Cited by: Papers (3)

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Large scale interaction data are frequently found in industrial applications related with Big Data. Due to the fact that few interactions commonly happen among numerous nodes in real scenes, such data can be quantified into a High-Dimensional and Incomplete (HDI) matrix where most entries are unknown. An alternating-direction-method-based nonnegative latent factor model can perform efficient and a...Show More

Alternating-Direction-Method of Multipliers-Based Adaptive Nonnegative Latent Factor Analysis

Yurong Zhong;Kechen Liu;Shangce Gao;Xin Luo

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 5 | Journal Article |

Iterative Role Negotiation via the Bilevel GRA++ With Decision Tolerance

Qian Jiang;Dongning Liu;Haibin Zhu;Shijue Wu;Naiqi Wu;Xin Luo;Yan Qiao

IEEE Transactions on Computational Social Systems

Year: 2024 | Volume: 11, Issue: 6 | Journal Article |

Cited by: Papers (3)

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Role negotiation (RN) is situated at the initial stage of the role-based collaboration (RBC) methodology and is independent of the subsequent agent evaluation and role assignment (RA) processes. RN is to determine the roles and the resource requirements for each role. In existing RBC-related research, RN is assumed to be static. This means that the roles and the resource requirements for each role...Show More

Iterative Role Negotiation via the Bilevel GRA++ With Decision Tolerance

Qian Jiang;Dongning Liu;Haibin Zhu;Shijue Wu;Naiqi Wu;Xin Luo;Yan Qiao

IEEE Transactions on Computational Social Systems

Year: 2024 | Volume: 11, Issue: 6 | Journal Article |

A Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm for Fast and Accurate Latent Factor Analysis

Ye Yuan;Jinli Li;Xin Luo

IEEE Transactions on Fuzzy Systems

Year: 2024 | Volume: 32, Issue: 7 | Journal Article |

Cited by: Papers (9)

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A stochastic gradient descent (SGD) based latent factor analysis (LFA) model can obtain superior performance when performing representation to a high-dimensional and incomplete (HDI) matrix, which is encountered in various big data-related applications cause of the great demand for describing the highly complex interactions among tremendous nodes. However, an SGD-based LFA model is often stacked b...Show More

A Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm for Fast and Accurate Latent Factor Analysis

Ye Yuan;Jinli Li;Xin Luo

IEEE Transactions on Fuzzy Systems

Year: 2024 | Volume: 32, Issue: 7 | Journal Article |

A Generalized Nesterov's Accelerated Gradient-Incorporated Non-Negative Latent-Factorization-of-Tensors Model for Efficient Representation to Dynamic QoS Data

Minzhi Chen;Renfang Wang;Yan Qiao;Xin Luo

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 3 | Journal Article |

Cited by: Papers (9)

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Dynamic Quality-of-Service (QoS) data can be efficiently represented by a Non-negative Latent-factorization-of-tensors model, which relies on a Non-negative and Multiplicative Update on Incomplete Tensors (NMU-IT) algorithm. Nevertheless, NMU-IT frequently encounters slow convergence and inefficient hyper-parameters selection. Targeting at overcome these critical defects, this paper proposed to im...Show More

A Generalized Nesterov's Accelerated Gradient-Incorporated Non-Negative Latent-Factorization-of-Tensors Model for Efficient Representation to Dynamic QoS Data

Minzhi Chen;Renfang Wang;Yan Qiao;Xin Luo

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 3 | Journal Article |

A Robust and Efficient Ensemble of Diversified Evolutionary Computing Algorithms for Accurate Robot Calibration

Tinghui Chen;Shuai Li;Yan Qiao;Xin Luo

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

Cited by: Papers (8)

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Industrial robots are regarded as essential instruments for advanced industry upgrading. The kinematic parameters of an industrial robot should be calibrated precisely to guarantee its absolute positioning accuracy, which can be implemented via an evolutionary computing (EC) algorithm; however, existing calibrators are mostly based on an EC algorithm with a homogeneous learning scheme, which may l...Show More

A Robust and Efficient Ensemble of Diversified Evolutionary Computing Algorithms for Accurate Robot Calibration

Tinghui Chen;Shuai Li;Yan Qiao;Xin Luo

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

Nianyin Zeng;Xinyu Li;Peishu Wu;Han Li;Xin Luo

IEEE/CAA Journal of Automatica Sinica

Year: 2024 | Volume: 11, Issue: 2 | Journal Article |

Cited by: Papers (24)

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Unmanned aerial vehicles (UAVs) have gained significant attention in practical applications, especially the low-altitude aerial (LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network (TDKD-Net) is proposed, where the TT-format TD (tensor decomposi...Show More

A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

Nianyin Zeng;Xinyu Li;Peishu Wu;Han Li;Xin Luo

IEEE/CAA Journal of Automatica Sinica

Year: 2024 | Volume: 11, Issue: 2 | Journal Article |

Adaptive Divergence-Based Non-Negative Latent Factor Analysis of High-Dimensional and Incomplete Matrices From Industrial Applications

Ye Yuan;Xin Luo;MengChu Zhou

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 2 | Journal Article |

Cited by: Papers (6)

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High-Dimensional and Incomplete (HDI) data are commonly seen in various big-data-related applications concerning the inherent non-negativity interactions among numerous nodes. A Non-negative Latent Factor Analysis (NLFA) model performs efficient representation learning to such HDI data. However, existing NLFA models all adopt a static divergence metric like Euclidean distance or α-β divergence to ...Show More

Adaptive Divergence-Based Non-Negative Latent Factor Analysis of High-Dimensional and Incomplete Matrices From Industrial Applications

Ye Yuan;Xin Luo;MengChu Zhou

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 2 | Journal Article |

Pseudo Gradient-Adjusted Particle Swarm Optimization for Accurate Adaptive Latent Factor Analysis

Xin Luo;Jiufang Chen;Ye Yuan;Zidong Wang

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2024 | Volume: 54, Issue: 4 | Journal Article |

Cited by: Papers (21)

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A latent factor analysis (LFA) model can be efficiently built via the stochastic gradient descent (SGD) algorithm to address high-dimensional and incomplete (HDI) data generated by various big data-related applications. However, its performance depends hugely on its hyperparameters, which is conventionally decided through the grid-search that yields extremely high-computational costs. Particle swa...Show More

Pseudo Gradient-Adjusted Particle Swarm Optimization for Accurate Adaptive Latent Factor Analysis

Xin Luo;Jiufang Chen;Ye Yuan;Zidong Wang

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2024 | Volume: 54, Issue: 4 | Journal Article |

Fuzzy k-Winner-Take-All Network for Competitive Coordination in Multirobot Systems

Long Jin;Yutong Li;Xiaoyan Zhang;Xin Luo

IEEE Transactions on Fuzzy Systems

Year: 2024 | Volume: 32, Issue: 4 | Journal Article |

Cited by: Papers (7)

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This study focuses on exploring the solution to coordination in multirobot systems faced with competitive states, where the competition behavior can be described as a $k$-winner-take-all ($k$WTA) operation. To address the $k$WTA problem, a fuzzy $k$WTA (F-$k$WTA) network is proposed, which incorporates a fuzzy mechanism into the network that adapts the convergence rate of the F-$k$WTA network base...Show More

Fuzzy k-Winner-Take-All Network for Competitive Coordination in Multirobot Systems

Long Jin;Yutong Li;Xiaoyan Zhang;Xin Luo

IEEE Transactions on Fuzzy Systems

Year: 2024 | Volume: 32, Issue: 4 | Journal Article |

A Novel Machine Learning System for Industrial Robot Arm Calibration

Zhibin Li;Shuai Li;Xin Luo

IEEE Transactions on Circuits and Systems II: Express Briefs

Year: 2024 | Volume: 71, Issue: 4 | Journal Article |

Cited by: Papers (3)

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The application of industrial robot arms in intelligent manufacturing is highly vigorous. Generally, robot arms have high repetitive positioning accuracy. However, they frequently suffer from large absolute positioning error, which can not be directly adopted in high-precision production activities, like chip and cell phone manufacturing. To address this critical issue, we first propose a novel cu...Show More

A Novel Machine Learning System for Industrial Robot Arm Calibration

Zhibin Li;Shuai Li;Xin Luo

IEEE Transactions on Circuits and Systems II: Express Briefs

Year: 2024 | Volume: 71, Issue: 4 | Journal Article |

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