Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Sugeno type controllers are universal controllers
Fuzzy Sets and Systems
Handbook of software reliability engineering
Handbook of software reliability engineering
Fuzzy system modeling by fuzzy partition and GA hybrid schemes
Fuzzy Sets and Systems
Algorithmic stability and sanity-check bounds for leave-one-out cross-validation
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Empirical Data Modeling in Software Engineering Using Radial Basis Functions
IEEE Transactions on Software Engineering
Fuzzy modelling and identification with genetic algorithm based learning
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Structure identification and parameter optimization for non-linear fuzzy modeling
Fuzzy Sets and Systems - Fuzzy systems
Adaptive Hierarchical Fair Competition (AHFC) Model For Parallel Evolutionary Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A GA-based fuzzy modeling approach for generating TSK models
Fuzzy Sets and Systems - Modeling and control
Fuzzy modelling through logic optimization
International Journal of Approximate Reasoning
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy
Fuzzy Sets and Systems
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A highly interpretable form of Sugeno inference systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
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
Decision making with imprecise parameters
International Journal of Approximate Reasoning
Expert Systems with Applications: An International Journal
Design of fuzzy radial basis function-based polynomial neural networks
Fuzzy Sets and Systems
A parallel evolving algorithm for flexible neural tree
Parallel Computing
Engineering Applications of Artificial Intelligence
Design of information granulation-based fuzzy radial basis function neural networks using NSGA-II
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Generation of a probabilistic fuzzy rule base by learning from examples
Information Sciences: an International Journal
A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection
Information Sciences: an International Journal
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In this paper, we develop a design methodology for information granulation-based genetically optimized fuzzy inference system, which deals with the tuning method with a variant identification ratio for structural as well as parametric optimization of the reasoning system. The tuning is carried out with the aid of the hierarchical fair competition-based parallel genetic algorithms and it employs the mechanism of information granulation. This version of the genetic algorithm is a multi-population variant of parallel genetic algorithms, which is particularly suitable for handling multimodal problems of high-dimensionality. The granulation of information is realized with the aid of the C-Means clustering algorithm. The concept of information granulation is applied to the formation of the fuzzy inference system in order to realize its structural optimization. Here we divide the input space in order to construct the premise part of the fuzzy rules. Subsequently the consequence part of each fuzzy rule is organized based on the center points (prototypes) of data group obtained as a result of clustering. In particular, this concerns the fuzzy inference system-related parameters, i.e., the number of input variables to be used in the fuzzy inference system, a collection of a specific subset of input variables, the number of membership functions used for each input variable, and the polynomial type (order) occurring at the consequence part of fuzzy rules. Making use of a mechanism of simultaneous tuning for the parameters, we construct an optimized fuzzy inference system related to its structural as well as parametric optimization. A comparative analysis demonstrates that the proposed methodology leads to improved results when compared with some conventional methods exploited in fuzzy modeling.