Article
Computer Science, Artificial Intelligence
Qiongdan Lou, Zhaohong Deng, Zhiyong Xiao, Kup-Sze Choi, Shitong Wang
Summary: This article proposes a new multilabel classification method called ML Takagi-Sugeno-Kang fuzzy system (ML-TSK FS) to improve classification performance. It uses fuzzy rules to model the relationship between features and labels.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Biochemical Research Methods
Xiaoyi Guo, Yizhang Jiang, Quan Zou
Summary: Therapeutic peptides have multiple benefits on human skeletal, digestive, and blood systems, including antibacterial properties and anti-inflammatory effects. In order to reduce resource consumption for wet experiments, computational-based methods have been developed for therapeutic peptide identification, and our proposed method, SSR-TSK-FS-WCS, shows promising performance on multiple therapeutic peptides and UCI datasets.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Computer Science, Theory & Methods
Guangdong Xue, Jian Wang, Bingjie Zhang, Bin Yuan, Caili Dai
Summary: This article proposes a Takagi-Sugeno-Kang fuzzy model with double groups of gates (DG-TSK) for simultaneous feature selection and rule extraction. The M-gate is used for feature selection, and each data point is transformed into a fuzzy rule, improving the effectiveness of the model.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Information Systems
Zhaohong Deng, Ya Cao, Qiongdan Lou, Kup-Sze Choi, Shitong Wang
Summary: The proposed monotonic relation-constrained Takagi-Sugeno-Kang fuzzy system classifier introduces a monotonic relation between inputs and outputs and uses Tikhonov regularization strategy to address convexity loss, resulting in better classification performance in handling monotonic datasets compared to existing methods.
INFORMATION SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Jun Li, Qibo Liu
Summary: A new prediction model combining interval type-2 Takagi-Sugeno-Kang fuzzy neural network model optimized by extended Kalman filter and SOM is proposed in this study to improve prediction accuracy by data partition and parameter optimization in each partition.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yuqi Cui, Yifan Xu, Ruimin Peng, Dongrui Wu
Summary: This article addresses the challenges of mini-batch gradient descent-based optimization in TSK fuzzy system optimization. It introduces a high-dimensional TSK (HTSK) algorithm using first layer normalization (LN) and rectified linear unit (ReLU) to improve the optimizer's performance. Experimental results show that this method significantly enhances the generalization performance on various datasets.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Basil Mohammed Al-Hadithi, Jose Miguel Adanez, Mayra Comina, Agustin Jimenez
Summary: In this work, a fuzzy predictive optimal control method is proposed for multivariable nonlinear systems with pure time delays. The method uses dynamic local linear state models obtained from fuzzy Takagi-Sugeno (T-S) modeling and observes the state using the Kalman Filter (KF). Compared to traditional Model Predictive Control (MPC) methods, the proposed approach calculates the control signal increment based on the error between a reference state vector and the prediction at N-steps of the state vector, resulting in computational savings and suitability for real-time applications.
IET CONTROL THEORY AND APPLICATIONS
(2022)
Article
Automation & Control Systems
Kaijian Xia, Yuanpeng Zhang, Yizhang Jiang, Pengjiang Qian, Jiancheng Dong, Hongsheng Yin, Raymond F. Muzic
Summary: The article introduces a novel Takagi-Sugeno-Kang (TSK) fuzzy system with low model complexity for multiview data pattern discovery. The proposed system includes a transformation matrix, sparsity regularizations, and the alternating direction method of multipliers to optimize the objective function, leading to promising performance in data pattern discovery with low model complexity.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Teofilo P. G. Mendes, Leizer Schnitman, Idelfonso Bessa dos Reis Nogueira, Ana Mafalda Almeida Peixoto Ribeiro, Alirio Egidio Rodrigues, Jose Miguel Loureiro, Marcio A. F. Martins
Summary: This manuscript presents a new fuzzy approach applied to Model Predictive Control (MPC). The first contribution of this work is a modification in the Takagi-Sugeno-Kang (TSK) structure that allows modeling IF-THEN rules without approximation. The second contribution is a new MPC formulation that guarantees system stability by combining optimization, state-space model, and terminal constraints. These methods were implemented on a Programmable Logic Controller and applied to control a physical DC motor.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Civil
Yi Gu, Kaijian Xia, Khin-Wee Lai, Yizhang Jiang, Pengjiang Qian, Xiaoqing Gu
Summary: In this paper, a novel method based on EEG is proposed for driving fatigue recognition in intelligent transportation. The method combines the techniques of result parameter learning and multi-view learning to improve the recognition performance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Bin Qin, Fu-Lai Chung, Shitong Wang
Summary: The study aims to enhance the generalization capability of zero-order TSK fuzzy classifiers through a novel knowledge adversarial attack model, which is theoretically justified for its strong generalization capability through dynamic regularization.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Engineering, Electrical & Electronic
Huimin Zhao, Yandong Wu, Wu Deng
Summary: This article explores and develops a new Takagi-Sugeno-Kang fuzzy inference system under the fuzzy broad learning framework to improve the accuracy and interpretability of fuzzy neural models. By using interpretable linguistic fuzzy rules and an incremental learning method, the issues of rule explosion and network structure redundancy are addressed.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Huabin Chen, Cheng-Chew Lim, Peng Shi
Summary: This article investigates the problems of robust exponential stabilization in mean square and H-infinity-based integral sliding mode controller design for uncertain stochastic T-S fuzzy switched time-delay systems. Sufficient conditions are developed to guarantee the robust exponential stability of the resulting closed-loop systems under synchronous and asynchronous switching. The effectiveness of the results is verified through two examples.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shixi Hou, Cheng Wang, Yundi Chu, Juntao Fei
Summary: Based on the global fast terminal sliding mode control, this article proposes a recurrent probabilistic compensation fuzzy neural network control scheme for handling nonlinear systems with uncertainties. The developed RPCFNN controller possesses superior nonlinearity handling capability and robustness.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Guangdong Xue, Jian Wang, Kai Zhang, Nikhil R. Pal
Summary: This study addresses the challenges of using T-norms for high-dimensional problems and proposes two HDFIS methods that can effectively handle high-dimensional datasets. Experimental results demonstrate their competitive performance on various datasets.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
