Hierarchical neuro-fuzzy quadtree models
Fuzzy Sets and Systems - Fuzzy models
Fast learning in networks of locally-tuned processing units
Neural Computation
Structure identification in complete rule-based fuzzy systems
IEEE Transactions on Fuzzy Systems
Taking on the curse of dimensionality in joint distributions using neural networks
IEEE Transactions on Neural Networks
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
Multiscale approximation with hierarchical radial basis functions networks
IEEE Transactions on Neural Networks
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In this paper we propose a new technique focused on the search of new architectures for modelling complex systems in function approximation problems, in order to avoid the exponential increase in the complexity of the system that is usual when dealing with many input variables. The new hierarchical network proposed, is composed of complete Radial Basis Function Networks (RBFNs) that are in charge of a reduced set of input variables. For the optimization of the whole net, we propose a new method to select the more important input variables, thus reducing the dimension of the input variable space for each RBFN. We also provide an algorithm which automatically finds the most suitable topology of the proposed hierarchical structure and selects the more important input variables for it. Therefore, our goal is to find the most suitable of the proposed families of hierarchical architectures in order to approximate a system from which a set of input/output (I/O) data has been extracted.