A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
A GA-based fuzzy modeling approach for generating TSK models
Fuzzy Sets and Systems - Modeling and control
Feature Selection Via Mathematical Programming
INFORMS Journal on Computing
Generating optimal adaptive fuzzy-neural models of dynamicalsystems with applications to control
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy-neural system modeling
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
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The problem of system input selection, dubbed in the literature as Type I Structure Identification problem, is addressed in this paper using an effective novel method. More specifically, the fuzzy curve technique, introduced by Lin and Cunningham (1995), is extended to an advantageous fuzzy surface technique; the latter is used for fast building a coarse model of the system from a subset of the initial candidate inputs. A simple genetic algorithm, enhanced with a local search operator, is used for finding an optimal subset of necessary and sufficient inputs by considering jointly more than one inputs. Extensive simulation results on both artificial data and real world data have demonstrated comparatively the advantages of the proposed method.