Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Constructing a fuzzy controller from data
Fuzzy Sets and Systems - Special issue on methods for data analysis in classificatin and control
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
Information Sciences—Informatics and Computer Science: An International Journal
SIAM Journal on Scientific Computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
On Clustering Validation Techniques
Journal of Intelligent Information Systems
A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Approaching the ocean color problem using fuzzy rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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Determining the concentrations of chlorophyll, suspended particulate matter and coloured dissolved organic matter in the sea water is basic to support the monitoring of upwelling phenomena, algae blooms, and changes in the marine ecosystem. Since these concentrations affect the spectral distribution of the solar light back-scattered by the water body, their estimation can be computed by using a set of remotely sensed multispectral measurements of the reflected sunlight. In this paper, the relation between the concentrations of interest and the average subsurface reflectances is modelled by means of a set of second-order Takagi-Sugeno (TS) fuzzy rules. Unlike first-order TS rules, which adopt linear functions as consequent, second-order TS rules exploit quadratic functions, thus improving the modelling capability of the rule in the subspace determined by the antecedent. First, we show how we can build a second-order TS model through a simple transformation, which allows estimating the consequent parameters using standard linear least-squares algorithms, and by adopting one of the most used methods proposed in the literature to generate first-order TS models. Then, we compare first-order and second-order TS models against mean square error and interpretability of rules. We highlight how second-order TS models allow us to achieve better approximation than first-order TS models though maintaining interpretability of the rules. Finally, we show how second-order TS models perform considerably better (the mean square error is lower by two orders of magnitude) than the specific implementations of radial basis function networks and multi-layer perceptron networks used in previous papers for the same application domain.