Universal approximation using radial-basis-function networks
Neural Computation
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
Output value-based initialization for radial basis function neural networks
Neural Processing Letters
Boosting the Performance of a Multiobjective Algorithm to Design RBFNNs Through Parallelization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
A First Approach to Birth Weight Prediction Using RBFNNs
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Applying multiobjective RBFNNs optimization and feature selection to a mineral reduction problem
Expert Systems with Applications: An International Journal
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Multiobjective RBFNNs designer for function approximation: an application for mineral reduction
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
A fuzzy-possibilistic fuzzy ruled clustering algorithm for RBFNNs design
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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Clustering algorithms have been applied in several disciplines successfully. One of those applications is the initialization of Radial Basis Functions (RBF) centers composing a Neural Network, designed to solve functional approximation problems. The Clustering for Function Approximation (CFA) algorithm was presented as a new clustering technique that provides better results than other clustering algorithms that were traditionally used to initialize RBF centers. Even though CFA improves performance against other clustering algorithms, it has some flaws that can be improved. Within those flaws, it can be mentioned the way the partition of the input data is done, the complex migration process, the algorithm's speed, the existence of some parameters that have to be set in order to obtain good solutions, and the convergence is not guaranteed. In this paper, it is proposed an improved version of this algorithm that solves the problems that its predecessor has using fuzzy logic successfully. In the experiments section, it will be shown how the new algorithm performs better than its predecessor and how important is to make a correct initialization of the RBF centers to obtain small approximation errors.