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Kernel contextual fuzzy rule model based on conditional input space partitioning driven by data reconstruction in autoencoder and randomization-based neural networks

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Knowledge-Based systems 2025年 320卷

作者： Zhang, Congcong Oh, Sung-Kwun Fu, Zunwei Pedrycz, Witold School of Mathematics and Statistics Linyi University Shandong Linyi276005 China Shandong Data Open Innovative Application Laboratory Linyi276005 China School of Automation and Electrical Engineering Linyi University Shandong Linyi276005 China School of Electrical & Electronic Engineering The University of Suwon Gyeonggi-do Hwaseong-si18323 Korea Republic of Department of Electronic Engineering Seokyeong University Seoul 02713 Korea Republic of Department of Computer Science The University of Suwon Gyeonggi-do Hwaseong-si18323 Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonAB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw 00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer/Istanbul Turkey

Fuzzy Rule-based Models (FRMs) have attracted significant interest in the field of machine learning owing to their modular architecture, robust design methodologies, and sound interpretability. This study introduces a novel Kernel Contextual Fuzzy Rule Model (KCFRM) designed to cope with regression tasks. A Kernel Contextual Fuzzy Clustering (KCFC) algorithm is proposed for conditional partitioning of the input space. Specifically, we incorporate the Mercer kernel into context fuzzy clustering to enhance the abilities of the model to distinguish, extract, and amplify useful features via nonlinear mapping, thereby generating more suitable information granules. Additionally, we employ an autoencoder's "encoding-decoding" mechanism to extract differences between data patterns and subsequently transform these into KCFC contexts via a conversion function, therefore leading to the creation of high-quality fuzzy sets. In the conclusion portion of fuzzy rules, conventional numerical or linear functions struggle to adequately describe the complex behavior present in local fuzzy regions. To mitigate this, we incorporate a Randomization-based Neural Network (RANN), capable of providing superior approximation capabilities and substantial computational efficiency. RANN overcomes traditional method constraints in representing complex behaviors within the fuzzy region. The inclusion of RANN results in more accurate and efficient conclusions for fuzzy rules. The uniqueness of this study lies in its holistic approach to designing fuzzy models with KCFC and RANN, improving the expressiveness of the rules and enhancing the generalization of the models. KCFRM's performance is assessed using various publicly available machine learning datasets, with experimental results underscoring its effectiveness and performance enhancements. © 2025

关键词： Fuzzy rules

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Data-Driven Learning and Control with Event-Triggered Measurements

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IEEE Transactions on Automatic Control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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design and Application of a One-Stop Mobile Application Sharing Platform for Steel Enterprises

Design and Application of a One-Stop Mobile Application Shar...

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Chinese Control Conference (CCC)

作者： Xinzhe Chen Luefeng Chen Jie Hu Qingfeng Yang Min Wu Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance.

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A framework for real-time traffic risk prediction incorporating cost-sensitive learning and dynamic thresholds

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Accident Analysis and Prevention 2025年 218卷 108087页

作者： Wu, Dan Xing, Lu Li, Ye Wong, Yiik Diew Lee, Jaeyoung Jay Dong, Changyin School of Traffic and Transportation Engineering Central South University Hunan Changsha410075 China School of Civil and Environmental Engineering Nanyang Technological University 639798 Singapore School of Automation Central South University Changsha China Department of Automation Tsinghua University Beijing China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-infrastructure Systems Changsha University of Science &Technology China Department of Civil Environmental & Construction Engineering University of Central Florida OrlandoFL32816 United States School of Aeronautics Northwestern Polytechnical University Xi'an710072 China National Key Laboratory of Aircraft Configuration Design Xi'an710072 China

In recent years, researchers have explored an innovative approach that leverages real vehicle trajectory data to simultaneously derive traffic state and risk level for real-time risk prediction, which is crucial for traffic safety. However, existing studies largely overlook the costs associated with incorrect predictions and the varying consequences of different misclassifications, which undermines the reliability of the obtained prediction results. To address these gaps, this study refined traffic risk classification into four levels (i.e., no, low, medium, and high risks) and incorporated misclassification costs into the prediction process through cost-sensitive learning (CSL). Furthermore, considering that multi-class prediction tasks often face performance degradation and increased risk level granularity worsens class imbalance, further amplifying this degradation, this study introduced dynamic thresholds (DTs) to improve model performance. The aforementioned cost coefficients and thresholds were pinpointed using a genetic algorithm (GA). Furthermore, the employed data, comprising variables related to traffic state and associated risk data, were sourced from the HighD dataset. Subsequently, CSL-DTs-based models were built by integrating CSL and DTs with four distinct baseline machine/deep learning models, and the prediction performance (e.g., precision) and computation time of these models were compared. Results show that, compared to the corresponding baseline models, the proposed models perform better for multi-class prediction tasks. Additionally, the computation time of the CSL-DTs-based models is found to be acceptable for real-time prediction purposes. Finally, to ensure the reliability of the results obtained through the GA optimization (e.g., avoiding local optima), convergence curves were plotted, confirming the robustness of the optimization process. A robustness analysis also demonstrates that the models are highly stable under slight perturbations of

关键词： Deep learning

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Mixed Fault-Tolerant Sampled-Data Fuzzy Control for Nonlinear Parabolic PDE systems Under Deception Attacks

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International Journal of Fuzzy systems 2022年第8期24卷 3513-3531页

作者： Li, Qian-Qian Wang, Zi-Peng Wu, Huai-Ning Li, Yueyang Li, Han-Xiong School of Electrical Engineering University of Jinan Jinan China Faculty of Information Technology Beijing University of Technology Beijing China Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Systems Engineering and Engineering Management City University of Hong Kong Kowloon Tong Hong Kong

This paper addresses mixed $$H_2/H_\infty$$ fault-tolerant sampled-data (SD) fuzzy control for nonlinear space-varying parabolic partial differential equation (PDE) system under deception attacks. Firstly, a T–S fuzzy PDE model is given to exactly describe the nonlinear space-varying parabolic PDE system. Secondly, a fault-tolerant SD fuzzy controller via the spatial linear matrix inequalities (SLMIs) is developed based on a Lyapunov functional which is continuous at sampling times but not necessary to be positive definite in sampling intervals such that the closed-loop PDE system is exponentially stable with a mixed $$H_2/H_\infty$$ performance. Then, to solve the SLMIs, the fault-tolerant SD fuzzy control problem for space-varying parabolic PDE system is formulated as linear matrix inequality feasibility problem. Furthermore, the design condition of the suboptimal mixed $$H_2/H_\infty$$ fault-tolerant SD controller subject to deception attacks can be derived by considering the property of membership functions. Lastly, two examples are given to illustrate the design method.

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Internet of UAVs to Automate Search and Rescue Missions in Post-Disaster for Smart Cities

Internet of UAVs to Automate Search and Rescue Missions in P...

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IEEE Symposium on Intelligent Vehicle

作者： Haishi Liu Yung Po Tsang Carman K. M. Lee Yutong Wang Fei-Yue Wang Laboratory for Artificial Intelligence in Design Hong Kong Science Park New Territories China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University China Institute of Automation Chinese Academy of Sciences Beijing China Macau University of Science and Technology Macao China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In natural disasters, the emergency rescue system is of utmost importance for the smart city development, but the search and rescue (SAR) leveraging unmanned aerial vehicles (UAVs) is under-explored. This study has established an Internet of UAVs architecture motivated by search and rescue activities, focusing on the path planning problem of UAV in three-dimensional urban disaster scenes. A mathematical model for UAV-based SAR missions has been built, aiming to use UAV platforms equipped with life-detection radars to search for survivors inside buildings. Our aim is to search for the most survivors in the shortest amount of time while ensuring coverage of the entire search area. Based on the mathematical model for SAR activities, the improved Multi-Verse Optimizer (IMVO) is used to solve the path of UAV. Finally, simulations were conducted in a photorealistic urban scenario, demonstrating the paths generated by the proposed method. The results indicate the potential of the generated path in terms of search time and the number of survivors discovered.

关键词： Solid modeling Three-dimensional displays Smart cities Trajectory planning Disasters Radar Autonomous aerial vehicles

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Rock-Climbing Fish Inspired Skeleton-Embedded Rigid-Flexible Coupling Suction Disc design for Adhesion Enhancement 16th

Rock-Climbing Fish Inspired Skeleton-Embedded Rigid-Flexible...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Tan, Wenjun Qin, Hengshen Zhang, Chuang Wang, Ruiqian Zhang, Yiwei Yang, Lianchao Chen, Qin Wang, Feifei Xi, Ning Liu, Lianqing State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chengdu Institute of Biology Chinese Academy of Sciences Sichuan Chengdu610042 China Department of Electrical and Electronic Engineering University of Hong Kong 999077 Hong Kong Emerging Technologies Institute Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Pokfulam999077 Hong Kong

ISBN: (纸本)9789819964888

Underwater high-performance biological adhesion systems have attracted increasing research interest to inspire engineered adhesion systems. Switchable underwater adhesion systems typically employ suction adhesion. However, achieving low pre-pressure negative pressure adhesion is still a challenge. An inspiring nature mode is provided by the rock-climbing fish, which needs little preload and can resist a pull-off force of 1000 times its own body weight. The pectoral and abdominal fins of rock-climbing fish have evolved to almost a flat suction cup. Through micro CT, we found that a unique skeletal network is embedded in the suction cup. This unique structure increases the stiffness of the suction cup and enhances its adhesion performance. Through numerical simulation, it was revealed that this rigid, flexible coupling structure enhances the ability of the suction cup to resist lateral pressure and increases the energy dissipation of the edge detachment of the suction cup. This work can shed light on the design of novel underwater adhesion systems and inspire underwater adhesion crawling robots. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Adhesion

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Adaptive NN impedance control for an SEA-driven robot

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Science China(Information Sciences) 2020年第5期63卷 221-223页

作者： Xinbo YU Wei HE Yanan LI Chengqian XUE Yongkun SUN Yu WANG School of Automation and Electrical Engineering University of Science and Technology Beijing Institute of Artificial Intelligence University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationUniversity of Science and Technology Beijing Department of Engineering and Design University of Sussex Institute of Automation Chinese Academy of Sciences

Dear editor,Series elastic actuators (SEAs) have been deliberately selected in collaborative robots intended for safe physical interaction with humans or unstructured environments [1]. This passive mechanical compliance guarantees an inertial decoupling between the link and actuator, thus decreasing kinetic energy involved in unexpected collisions with environments [2]. SEAs are widely used in cooper-

关键词： simulation Adaptive NN impedance control for an SEA-driven robot

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MBSE Approach to Early Naval Ship design Exploration with Power Electronic Power Distribution System in Power and Energy Corridors

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IEEE Transactions on Transportation Electrification 2025年

作者： Cuzner, Robert Chalfant, Julie Gross, David Shabani, Hamed Ali, Naqash Steurer, Mischa University of Wisconsin-Milwaukee Department of Electrical Engineering MilwaukeeWI53211 United States Design Laboratory of the MIT Sea Grant College Program Massachusetts Institute of Technology CambridgeMA02139 United States Florida State University Center for Advanced Power Systems TallahasseeFL32310 United States

The concept development of any system includes the definition of various parameters of interest and determination of the threshold (satisfactory) and goal (desired) value for each. These are classically organized into Measures of Effectiveness, Measures of Performance (MOPs), & Technical Performance Measures (TPMs). These form the basis of performance requirements, the means for evaluating design, and the measure by which systems are validated. A cross-cutting sub-set of MOPs and TPMs, Key Performance Parameters (KPPs) can define the trade space within which to optimize the emerging solution. Applying this systematic approach is substantially complicated when the system of interest aims for unprecedented capabilities from emerging technologies, which is the situation for electric ship research. This paper presents an Integration Framework that effectively maps out a process leading to defined parameter objectives, which is being developed and used in the U.S. department of Navy sponsored Power Electronic Power Distribution System (PEPDS) research initiative. The method then integrates the Model-Based systems engineering (MBSE) system model with genetic algorithm trade space exploration tools. © 2025 IEEE. All rights reserved.

关键词： Warships

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Joint Deployment and Resource Allocation for Multi-AeBS Networks: A Two-Timescale Optimization Framework Using MADRL

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IEEE Transactions on Communications 2024年

作者： Zhao, Yikun Zhou, Fanqin Feng, Lei Sun, Yao Li, Wenjing Lim, Wei Yang Bryan Xiong, Zehui Mao, Shiwen Han, Zhu State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom Nanyang Technological University Alibaba Group and Alibaba-NTU Joint Research Institute Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheless, the scarce spectrum resources and difficulty in accessing global information bring necessity and challenges to the deployment and resource allocation of AeBSs. In this paper, we propose a practical two-timescale framework to solve the resource allocation and deployment optimization problem in multi-AeBS networks. Specifically, the subcarrier allocation problem is first transformed into a many-to-one matching game coupled with power allocation and solved in a small timescale. Then, in a large timescale, the AeBS deployment subproblem is transformed into a distributed partially observable Markov decision process (Dec-POMDP), and then a novel multi-agent hypergraph convolutional deep reinforcement learning (MAHGCDRL) is proposed to solve this problem. The proposed MAHGCDRL extracts features of neighboring AeBSs through hypergraph convolutional networks, enabling AeBS agents to achieve better coordination in a distributed manner. Simulation results show that our proposed approach can attain a higher sum rate, and the proposed MAHGCDRL algorithm achieves better learning performance compared to the existing benchmarks in the literature. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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