On fuzzy implication operators
Fuzzy Sets and Systems
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Neuro-fuzzy architectures and hybrid learning
Neuro-fuzzy architectures and hybrid learning
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Flexible Neuro-fuzzy Systems: Structures, Learning and Performance Evaluation (Kluwer International Series in Engineering and Computer Science)
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Evolutionary Methods for Designing Neuro-fuzzy Modular Systems Combined by Bagging Algorithm
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Evolutionary Methods to Create Interpretable Modular System
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Evolutionary designing of logic-type fuzzy systems
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
On automatic design of neuro-fuzzy systems
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Evolutionary learning for neuro-fuzzy ensembles with generalized parametric triangular norms
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
On designing of flexible neuro-fuzzy systems for nonlinear modelling
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
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In this paper we present an evolutionary method for learning fuzzy rule base systems as an alternative to gradient methods. It is known that the backpropagation algorithm can be trapped in local minima. We use evolutionary strategies (μ,λ) with a novel method for generating an initial population. The results of simulations illustrate efficiency of our method.