A resource-allocating network for function interpolation
Neural Computation
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Prediction of Chaotic Time-Series with a Resource-Allocating RBF Network
Neural Processing Letters
Fuzzy modeling with hybrid systems
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Networks
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Generalized Convergence Models for Tournament- and (mu, lambda)-Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Convergence Models of Genetic Algorithm Selection Schemes
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Fast learning in networks of locally-tuned processing units
Neural Computation
Evolutionary multiobjective optimization for generating an ensemble of fuzzy rule-based classifiers
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Fuzzy function approximation with ellipsoidal rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simplifying fuzzy rule-based models using orthogonal transformationmethods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A systematic approach to a self-generating fuzzy rule-table forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
International Journal of Approximate Reasoning
A new probabilistic fuzzy model: Fuzzification--Maximization (FM) approach
International Journal of Approximate Reasoning
Piecewise parametric polynomial fuzzy sets
International Journal of Approximate Reasoning
Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms
Fuzzy Sets and Systems
Looking for a good fuzzy system interpretability index: An experimental approach
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Intelligent data analysis and model interpretation with spectral analysis fuzzy symbolic modeling
International Journal of Approximate Reasoning
Design of fuzzy radial basis function-based polynomial neural networks
Fuzzy Sets and Systems
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The identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory. An important characteristic that distinguishes fuzzy systems from other techniques in this area is their transparency and interpretability. Especially in the construction of a fuzzy system from a set of given training examples, little attention has been paid to the analysis of the trade-off between complexity and accuracy maintaining the interpretability of the final fuzzy system. In this paper a multi-objective evolutionary approach is proposed to determine a Pareto-optimum set of fuzzy systems with different compromises between their accuracy and complexity. In particular, two fundamental and competing objectives concerning fuzzy system modeling are addressed: fuzzy rule parameter optimization and the identification of system structure (i.e. the number of membership functions and fuzzy rules), taking always in mind the transparency of the obtained system. Another key aspect of the algorithm presented in this work is the use of some new expert evolutionary operators, specifically designed for the problem of fuzzy function approximation, that try to avoid the generation of worse solutions in order to accelerate the convergence of the algorithm.