Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Fuzzy-pattern recognition for automatic detection of different teeth substances
Fuzzy Sets and Systems - Special issue on methods for data analysis in classificatin and control
An efficient fuzzy based neuro: genetic algorithm for stock market prediction
International Journal of Hybrid Intelligent Systems
A discrete model for color naming
EURASIP Journal on Applied Signal Processing
Fast learning in networks of locally-tuned processing units
Neural Computation
Novel modified fuzzy c-means algorithm with applications
Digital Signal Processing
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy
Fuzzy Sets and Systems
A systematic approach to a self-generating fuzzy rule-table forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Structure identification in complete rule-based fuzzy systems
IEEE Transactions on Fuzzy Systems
Multiobjective identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
Fuzzy color histogram and its use in color image retrieval
IEEE Transactions on Image Processing
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
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Color change modelling is an essential problem in a wide range of colorimetric applications. Specifically, the approximation to the color expected after a physical chemical or natural color is essential in industry and in color science in general. This work proposes a modified neuro-fuzzy approach as a solution for a color change modelling problem. Neuro-fuzzy systems are well known methods for data modelling; their main advantage is their ability to provide an accurate solution from which an interpretable set of rules that can be extracted and used by the scientists. However these models have the problem that the global approximation optimization can lead to a deficient interpretation of the rules extracted from the model. This work proposes a modified neuro-fuzzy model that performs a simultaneous global and local modelling; this property is reached thanks to a special partitioning of the input space in the system. Specifically, the proposed methodology will be applied to a very important problem from the clinical and odontologic point of view as it is the modelling of the tooth color variation after a bleaching process. The availability of tools that help to predict these changes, from the initial chromaticity of the tooth, can solve the problem of lack of information on the expected tooth color after a specific treatment and help the dentist in the decision making on the most appropriate protocol for this treatment and in providing adequate information to the patient.