Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Extracting Interpretable Fuzzy Rules from RBF Networks
Neural Processing Letters
Fast learning in networks of locally-tuned processing units
Neural Computation
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy
Fuzzy Sets and Systems
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Fuzzy sets of rules for system identification
IEEE Transactions on Fuzzy Systems
Multiobjective identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
IEEE Transactions on Fuzzy Systems
Unification of neural and wavelet networks and fuzzy systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Efficient learning algorithms for three-layer regular feedforward fuzzy neural networks
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In the problem of modelling Input/Output data using neuro-fuzzy systems, the performance of the global model is normally the only objective optimized, and this might cause a misleading performance of the local models. This work presents a modified radial basis function network that maintains the optimization properties of the local sub-models whereas the model is globally optimized, thanks to a special partitioning of the input space in the hidden layer performed to carry out those objectives. The advantage of the methodology proposed is that due to those properties, the global and the local models are both directly optimized. A learning methodology adapted to the proposed model is used in the simulations, consisting of a clustering algorithm for the initialization of the centers and a local search technique.