Structure identification of fuzzy model
Fuzzy Sets and Systems
The nature of statistical learning theory
The nature of statistical learning theory
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A course in fuzzy systems and control
A course in fuzzy systems and control
A clustering algorithm for fuzzy model identification
Fuzzy Sets and Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
Supervised fuzzy clustering for rule extraction
IEEE Transactions on Fuzzy Systems
An overview of statistical learning theory
IEEE Transactions on Neural Networks
On support vector regression machines with linguistic interpretation of the kernel matrix
Fuzzy Sets and Systems
A time-domain-constrained fuzzy clustering method and its application to signal analysis
Fuzzy Sets and Systems
IEEE Transactions on Information Technology in Biomedicine
Robust prediction with ANNBFIS system
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Computerized analysis of fetal heart rate signals as the predictor of neonatal acidemia
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
In this paper, a new learning method tolerant to imprecision is introduced and used in neuro-fuzzy modeling. This method can be called ε-insensitive learning, where in order to fit the fuzzy model to real data, a weighted ε-insensitive loss function is used. The proposed method makes it possible to exclude an intrinsic inconsistency of neuro-fuzzy modeling, where zero-tolerance learning is used to obtain a fuzzy model tolerant to imprecision. The ε-insensitive learning leads to a model with the minimal Vapnik-Chervonenkis dimension (complexity), which results in improving generalization ability of this system and its robustness to outliers. Finally, numerical examples are given to demonstrate the validity of the introduced method.