Structure identification of fuzzy model
Fuzzy Sets and Systems
Fuzzy Modeling for Control
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Approximative fuzzy rules approaches for classification with hybrid-GA techniques
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Toward a framework for the specification of hybrid fuzzy modeling: Research Articles
International Journal of Intelligent Systems - Soft Computing for Modeling, Simulation, and Control of Nonlinear Dynamical Systems
A methodology for extracting objective color from images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Constructing a user-friendly GA-based fuzzy system directly from numerical data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
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The context of this paper is the auto calibration of a CCD low cost camera of a robotic pet. The underlined idea of the auto calibration is to imitate the human eye capabilities, which is able to accommodates changing lighting conditions, and only when all functionalities works properly, light is converted to impulses to the brain where the image is sensed. In order to choose the more appropriated camera's parameters, a fuzzy rules model has been generated following a neuro-fuzzy approach. This model classifies images into five classes: from very dark, to very light. This is the first step to the generation of a subsequent fuzzy controller able to change the camera setting in order to improve the image received from an environment with changing lighting conditions.