Article
Computer Science, Artificial Intelligence
Alejandro Pena, Juan C. Tejada, Juan David Gonzalez-Ruiz, Lina Maria Sepulveda-Cano, Francisco Chiclana, Fabio Caraffini, Mario Gongora
Summary: This paper presents a model for a serial robotic system with flexible joints (RFJ) using Euler-Lagrange equations. It also proposes a Stochastic Flexible-Adaptive Neural Integrated System (SF-ANFIS) for identifying and controlling the RFJ. The SF-ANFIS model shows better performance in both identification and control stages compared to the MADALINE model, with improved statistical indices and the ability to cancel oscillations.
APPLIED SOFT COMPUTING
(2023)
Article
Mathematics
Haifeng Huang, Mohammadamin Shirkhani, Jafar Tavoosi, Omar Mahmoud
Summary: This paper presents a new method for comprehensive stabilization and control system design for stochastic nonlinear systems using a type-3 fuzzy neural network to estimate parameters. Simulation results show that the proposed method has a good performance and can be applied to systems in this class.
Article
Computer Science, Information Systems
Qi Pan, Juntao Fei
Summary: This study presents an adaptive Super-Twisting sliding mode control approach using an output feedback fuzzy neural network for dynamic systems. Experimental results demonstrate that the proposed controller achieves better harmonic suppression and steady-state and dynamic properties.
Article
Mathematics
Ayman A. Aly, Kuo-Hsien Hsia, Fayez F. M. El-Sousy, Saleh Mobayen, Ahmed Alotaibi, Ghassan Mousa, Dac-Nhuong Le
Summary: This study investigates the desired tracking control of the upper-limb exoskeleton robot system under model uncertainty and external disturbance. An adaptive neural network using a backstepping control strategy is designed to compensate for the model uncertainty and external disturbances.
Article
Mathematics
Xiaoyu Gong, Wen Fu, Xingao Bian, Juntao Fei
Summary: An adaptive backstepping terminal sliding mode control method based on a multiple-layer fuzzy neural network is proposed for nonlinear systems with parameter variations and external disturbances. The proposed neural network is used to estimate the nonlinear function and reduce the switching term gain. It has a strong learning ability and high approximation accuracy. The control signal is stabilized using an additional parameter adaptive law derived by the adaptive projection algorithm. Terminal sliding mode control is introduced to ensure finite-time convergence of the tracking error. Simulation results on a DC-DC buck converter model demonstrate the effectiveness and superiority of the proposed control method.
Article
Engineering, Electrical & Electronic
Hoda Sorouri, Mostafa Sedighizadeh, Arman Oshnoei, Rahmat Khezri
Summary: This paper proposes a backstepping controller with a nonlinear disturbance observer to regulate the output voltage of a Buck DC-DC converter in a DC microgrid. The controller effectively dampens the ripples caused by load condition changes and adapts to uncertainties in the microgrid using an artificial neural network. The effectiveness of the proposed control method is verified through simulations and experiments.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Peng Yin, Jinzhou Chen, Hongwen He
Summary: In this paper, a nonlinear air supply system model integrated with the fuel cell stack voltage model is built. Conventional PID controls for the oxygen excess ratio are implemented, and fuzzy logic inference and neural network algorithm are integrated into the PID controller to tune the gain coefficients. Simulation results show that the fuzzy PID controller with seven subsets can improve the dynamic responses of the fuel cells in both constant and variable OER controls.
Article
Mathematics
Lunhaojie Liu, Wen Fu, Xingao Bian, Juntao Fei
Summary: In this work, a novel fuzzy neural network (NFNN) with a long short-term memory (LSTM) structure was proposed for estimation and control of nonlinear systems. The introduced LSTM recursive structure enhanced the learning and estimation capability of the NFNN. Simulation results showed that the proposed method outperformed existing methods in terms of performance and robustness.
Article
Computer Science, Information Systems
Tae-Yeun Kim, Sung-Hwan Kim, Hoon Ko
Summary: The study utilized brain wave information for intelligent upper limb rehabilitation, optimizing the system for reliability and performance. Results showed high accuracy in recognizing user intentions, facilitating continued rehabilitation exercise and improving quality of life.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Tian-He Wang, Xi-Mei Zhao, Hong-Yan Jin
Summary: This paper adopts an intelligent fractional-order backstepping control (IFOBC) method combining FOBC and TSKFNN to achieve high-performance servo control for PMLSM. BC is used for global regulation and position tracking, while FOBC is designed to enhance convergence speed and control accuracy. TSKFNN is introduced to estimate uncertainties and a robust compensator is developed to improve system robustness, with adaptive learning algorithms ensuring system stability.
ELECTRICAL ENGINEERING
(2021)
Article
Chemistry, Multidisciplinary
Qiuyue Qin, Guoqin Gao
Summary: The research proposes a new methodology for trajectory tracking control of industrial robots, aiming to enhance robustness and active adaptability. By introducing an adaptive fuzzy neural network and subtractive clustering algorithm, the uncertainties, synchronization and flexible control issues of UBSHR are addressed effectively.
APPLIED SCIENCES-BASEL
(2021)
Article
Automation & Control Systems
Guoyu Zuo, Jiyong Zhou, Daoxiong Gong, Gao Huang
Summary: This article proposes a servo control strategy for robot joints based on the incremental Bayesian fuzzy broad learning system (IBFBLS), which solves the problems of computational redundancy, limited prediction accuracy, and insufficient generalization capability in existing intelligent servo control strategies. The proposed control strategy has good self-learning and generalization abilities and achieves precise joint servo control through Bayesian inference. Simulation and experiments demonstrate the superiority of the proposed control strategy in terms of tracking accuracy, stability, and convergence compared to other servo control methods.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Engineering, Electrical & Electronic
Shih-Gang Chen, Faa-Jeng Lin, Chia-Hui Liang, Chen-Hao Liao
Summary: A novel maximum power factor control system is proposed for a high-performance synchronous reluctance motor drive system, utilizing a current angle controller with stator resistance and stator flux estimators. By employing a recurrent Chebyshev fuzzy neural network current angle controller, online optimal power factor points of the SynRM can be effectively obtained under different operating conditions.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2021)
Article
Engineering, Aerospace
Mihai Lungu
Summary: A new third-order nonlinear dynamics solving method is proposed in this paper for DGCMGs, along with a feed-forward neural network observer and two novel control architectures using Lyapunov theory and backstepping method. These approaches enhance the robustness of the control system and reject disturbances effectively.
AEROSPACE SCIENCE AND TECHNOLOGY
(2021)
Article
Energy & Fuels
Faa-Jeng Lin, Ming-Shi Huang, Yu-Chen Chien, Shih-Gang Chen
Summary: In this study, an intelligent servo drive system for a PMASynRM is developed using a RWFNN with intelligent backstepping control. The system includes a MTPA controlled PMASynRM servo drive and a BSC system to accurately follow the desired position. To overcome the challenge of designing an efficient BSC, an RWFNN is introduced as an approximation approach, and an enhanced adaptive compensator is incorporated to handle approximation errors effectively. The proposed IBSCRWFNN demonstrates remarkable effectiveness and robustness in controlling the PMASynRM servo drive.
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)