Neural network implementation of fuzzy logic
Fuzzy Sets and Systems
Fuzzy engineering
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Extracting Interpretable Fuzzy Rules from RBF Networks
Neural Processing Letters
Neural Computation
Growing kernel-based self-organized maps trained with supervised bias
Intelligent Data Analysis
Symbolic adaptive neuro-fuzzy inference for data mining of heterogenous data
Intelligent Data Analysis
Fuzzy function approximation with ellipsoidal rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms
IEEE Transactions on Fuzzy Systems
Generating an interpretable family of fuzzy partitions from data
IEEE Transactions on Fuzzy Systems
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification
IEEE Transactions on Neural Networks
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The maximization of the performance of the most if not all the fuzzy identification techniques is usually expressed in terms of the generalization performance of the derived neuro-fuzzy construction. Support Vector algorithms are adapted for the identification of a Support Vector Fuzzy Inference (SVFI) system that obtains robust generalization performance. However, these SVFI rules usually lack of interpretability. The accurate set of rules can be approximated with a simpler interpretable fuzzy system that can present insight to the more important aspects of the data. The interpretable fuzzy system construction algorithms receive an a priori description of a set of fuzzy sets that describe the linguistic aspects of the input variables as they are usually perceived by the human experts. In the case of the interpretable fuzzy sets an adaptive an algorithm for building them automatically is presented here. After the construction of the interpretable fuzzy partitions, the developed algorithms extract from the SVFI rules a small and concise set of interpretable rules. Finally, the Pseudo-Outer Product (POP) fuzzy rule selection orders the interpretable rules by using a Hebbian like evaluation in order to present the designer with the most capable rules.