Pattern Recognition Letters
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
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
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Linguistic models as a framework of user-centric system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
A transformed input-domain approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
Genetically optimized fuzzy polynomial neural networks
IEEE Transactions on Fuzzy Systems
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems
IEEE Transactions on Neural Networks
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
A new probabilistic fuzzy model: Fuzzification--Maximization (FM) approach
International Journal of Approximate Reasoning
A new evolutionary particle filter for the prevention of sample impoverishment
IEEE Transactions on Evolutionary Computation
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Fuzzy clustering forms a cornerstone of fuzzy (granular) modeling. The clusters (prototypes) are viewed as a blueprint of the model that is further refined through a number of detailed estimation techniques. In this study, we claim that while clustering is indisputable essential to fuzzy modeling, the essence of clustering mechanisms supporting this process of information granulation is not compatible with the character of the task at hand. In modeling, the required constructs are inherently direction-sensitive (that is we clearly distinguish between input and output variables). On the other hand, fuzzy clustering is direction neutral and during the formation of the clusters does not take this into consideration. We re-formulate the clustering so that the directionality aspect can be addressed in the optimization process. This leads to a new, augmented objective function to be minimized. A detailed algorithm is derived. As the directional sensitivity of the clustering method gives rise to different numbers of clusters in the input and output space, it becomes necessary to identify a mapping between these clusters which in turn gives rise to some allocation problem. Because of its inherently combinatorial character, the proposed solution is obtained through some genetic optimization. Comprehensive experiments demonstrate the performance of the approach and compare it with some of the generic version of the FCM clustering.