Results 261 to 270 of about 1,671,237 (334)

A CNN-Based Born-Again TSK Fuzzy Classifier Integrating Soft Label Information and Knowledge Distillation

IEEE Transactions on Fuzzy Systems, 2023
This article proposes a CNN-based born-again Takagi–Sugeno–Kang (TSK) fuzzy classifier denoted as CNNBaTSK. CNNBaTSK achieves the following distinctive characteristics: 1) CNNBaTSK provides a new perspective of knowledge distillation with a noniterative ...
Yunliang Jiang, Jiangwei Weng, Xiongtao Zhang, Zhen Yang, Wenjun Hu   +4 more
semanticscholar   +3 more sources

Deep Takagi–Sugeno–Kang Fuzzy Classifier With Shared Linguistic Fuzzy Rules

IEEE Transactions on Fuzzy Systems, 2018
In many practical applications of classifiers, not only high accuracy but also high interpretability is required. Among a wide variety of existing classifiers, Takagi–Sugeno–Kang (TSK) fuzzy classifiers may be one of the best choices for achieving a good
Yuanpeng Zhang, H. Ishibuchi, Shitong Wang   +2 more
semanticscholar   +3 more sources

Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification

Renewable Energy, 2018
In this paper, a Multiclass Adaptive Neuro-Fuzzy Classifier (MC-NFC) for fault detection and classification in photovoltaic (PV) array has been developed. Firstly, to show the generalization capability in the automatic faults classification of a PV array
A. Belaout, F. Krim, A. Mellit, B. Talbi, Abderrazak Arabi   +4 more
semanticscholar   +3 more sources

Dual-Fuzzy-Classifier-Based Evolutionary Algorithm for Expensive Multiobjective Optimization

IEEE Transactions on Evolutionary Computation, 2023
Multiobjective evolutionary algorithms (MOEAs) have been widely used to solve multiobjective optimization problems (MOPs). Conventional MOEAs usually require a large number of function evaluations (FEs) for evaluating the quality of solutions.
Jinyuan Zhang, Linjun He, H. Ishibuchi
semanticscholar   +1 more source

Fast Training of Adversarial Deep Fuzzy Classifier by Downsizing Fuzzy Rules With Gradient Guided Learning

IEEE transactions on fuzzy systems, 2022
While our recent deep fuzzy classifier DSA-FC, which stacks adversarial interpretable Takagi–Sugeno–Kang fuzzy subclassifiers, shares its promising classification, its training speed will become very slow and even intolerable for large-scale datasets ...
Suhang Gu, C. Vong, P. Wong, Shitong Wang   +3 more
semanticscholar   +1 more source

Optimal design of a general type-2 fuzzy classifier for the pulse level and its hardware implementation

Engineering applications of artificial intelligence, 2021
. Nowadays, soft computing has been of great help in solving real-world problems and satisfying the needs in our everyday life. We require more than ever the development and implementation of models and techniques that assist in medical issues due to the
O. Carvajal, P. Melin, I. Miramontes, G. Prado-Arechiga   +3 more
semanticscholar   +1 more source

Comparative analysis of fuzzy classifier and ANN with histogram features for defect detection and classification in planetary gearbox

Applied Soft Computing, 2021
The planetary gearbox plays a vital role in many heavy-duty power transmission systems. It is essential to monitor such systems for smooth and continuous operations to anticipate machine downtime, production loss and to schedule maintenance.
S. S. Hameed, V. Muralidharan, B. K. Ane
semanticscholar   +1 more source

Fuzzy Classifier Design for Development Tendency of Hot Metal Silicon Content in Blast Furnace

IEEE Transactions on Industrial Informatics, 2018
Junpeng Li, C. Hua, Yana Yang, X. Guan
semanticscholar   +3 more sources

Interpretable Deep Convolutional Fuzzy Classifier

IEEE transactions on fuzzy systems, 2020
While deep learning has proven to be a powerful new tool for modeling and predicting a wide variety of complex phenomena, those models remain incomprehensible black boxes.
Mojtaba Yeganejou, S. Dick, James Miller
semanticscholar   +1 more source

A Deep-Ensemble-Level-Based Interpretable Takagi–Sugeno–Kang Fuzzy Classifier for Imbalanced Data

IEEE Transactions on Cybernetics, 2020
Existing research reveals that the misclassification rate for imbalanced data depends heavily on the problematic areas due to the existence of small disjoints, class overlap, borderline, and rare data samples. In this study, by stacking zero-order Takagi–
Guanjin Wang, Ta Zhou, K. Choi, Jie Lu
semanticscholar   +1 more source