Universal approximation using radial-basis-function networks
Neural Computation
A global learing algorithm for a RBF network
Neural Networks
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Neural Networks
A possibilistic approach to RBFN centers initialization
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Improved possibilistic C-means clustering algorithms
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Hi-index | 0.00 |
This paper presents a new approach to the problem of designing Radial Basis Function Neural Networks (RBFNNs) to approximate a given function. The presented algorithm focuses in the first stage of the design where the centers of the RBFs have to be placed. This task has been commonly solved by applying generic clustering algorithms although in other cases, some specific clustering algorithms were considered. These specific algorithms improved the performance by adding some elements that allow them to use the information provided by the output of the function to be approximated but they did not add problem specific knowledge. The novelty of the new developed algorithm is the combination of a fuzzy-possibilistic approach with a supervising parameter and the addition of a new migration step that, through the generation of RBFNNs, is able to take proper decisions on where to move the centers. The algorithm also introduces a fuzzy logic element by setting a fuzzy rule that determines the input vectors that influence each center position, this fuzzy rule considers the output of the function to be approximated and the fuzzy-possibilistic partition of the data.