Robust regression and outlier detection
Robust regression and outlier detection
Algorithms for clustering data
Algorithms for clustering data
A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
Pattern Recognition Letters
Constructing a fuzzy controller from data
Fuzzy Sets and Systems - Special issue on methods for data analysis in classificatin and control
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Modeling for Control
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
A Clustering Performance Measure Based on Fuzzy Set Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust TSK fuzzy modeling approach using noise clustering concept for function approximation
CIS'04 Proceedings of the First international conference on Computational and Information Science
A TSK fuzzy inference algorithm for online identification
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Fuzzy function approximation with ellipsoidal rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Function approximation using fuzzy neural networks with robust learning algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ϵ-insensitive fuzzy c-regression models: introduction to ϵ-insensitive fuzzy modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid approach of selecting hyperparameters of support vector machine for regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Robust TSK fuzzy modeling for function approximation with outliers
IEEE Transactions on Fuzzy Systems
TSK-fuzzy modeling based on ϵ-insensitive learning
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Fuzzy min-max neural networks -- Part 2: Clustering
IEEE Transactions on Fuzzy Systems
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
Robust support vector regression networks for function approximation with outliers
IEEE Transactions on Neural Networks
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
Robust H∞ control for Van de Vusse reactor via T-S fuzzy bilinear scheme
Expert Systems with Applications: An International Journal
Radial basis function networks with hybrid learning for system identification with outliers
Applied Soft Computing
| Hi-index | 12.06 |
This study proposes a hybrid robust approach for constructing Takagi-Sugeno-Kang (TSK) fuzzy models with outliers. The approach consists of a robust fuzzy C-regression model (RFCRM) clustering algorithm in the coarse-tuning phase and an annealing robust back-propagation (ARBP) learning algorithm in the fine-tuning phase. The RFCRM clustering algorithm is modified from the fuzzy C-regression models (FCRM) clustering algorithm by incorporating a robust mechanism and considering input data distribution and robust similarity measure into the FCRM clustering algorithm. Due to the use of robust mechanisms and the consideration of input data distribution, the fuzzy subspaces and the parameters of functions in the consequent parts are simultaneously identified by the proposed RFCRM clustering algorithm and the obtained model will not be significantly affected by outliers. Furthermore, the robust similarity measure is used in the clustering process to reduce the redundant clusters. Consequently, the RFCRM clustering algorithm can generate a better initialization for the TSK fuzzy models in the coarse-tuning phase. Then, an ARBP algorithm is employed to obtain a more precise model in the fine-tuning phase. From our simulation results, it is clearly evident that the proposed robust TSK fuzzy model approach is superior to existing approaches in learning speed and in approximation accuracy.